Cities play a leading role in climate change actions and solutions, yet city-level case study sources remain fragmented, biased towards large cities, and inaccessible to local practitioners, notably in the Global South. In response, the Urban Climate Change Research Network (UCCRN) is developing a City Solutions Case Study Atlas (City CSA), a centralized and searchable online platform that integrates diverse case studies focused on climate solutions with an interactive global map containing multiple data layers. The UCCRN City CSA provides an evidence base for academics, urban policymakers, city practitioners, city networks, civil society, and the financial sector to facilitate equitable knowledge transfer and support the development and implementation of context-specific, science-informed urban climate solutions. This paper presents the framework, metadata, and structure of the UCCRN City CSA and assesses metadata search filters and large language model (LLM)-assisted discourse analysis as complementary tools for three user types.

We summarize historic New York City (NYC) climate change trends and provide the latest scientific analyses on projected future changes based on a range of global greenhouse gas emissions scenarios. Building on previous NPCC assessment reports, we describe new methods used to develop the projections of record for sea level rise, temperature, and precipitation for NYC, across multiple emissions pathways and analyze the issue of the "hot models" associated with the 6th phase of the Coupled Model Intercomparison Project (CMIP6) and their potential impact on NYC's climate projections. We describe the state of the science on temperature variability within NYC and explain both the large-scale and regional dynamics that lead to extreme heat events, as well as the local physical drivers that lead to inequitable distributions of exposure to extreme heat. We identify three areas of tail risk and potential for its mischaracterization, including the physical processes of extreme events and the effects of a changing climate. Finally, we review opportunities for future research, with a focus on the hot model problem and the intersection of spatial resolution of projections with gaps in knowledge in the impacts of the climate signal on intraurban heat and heat exposure.

Many fundamental aspects of New York State's climate have already begun to change, and the changes are projected to continue-and in some cases, accelerate-throughout the 21st century. This chapter explores observed and projected changes in a variety of physical variables that relate directly to weather and climate, starting with average and extreme air temperature and proceeding to the associated effects on precipitation, extreme events, and core properties of New York's coastal and inland waters. These climate attributes and hazards lead to impacts throughout the eight sectors of this assessment.

New York City (NYC) faces many challenges in the coming decades due to climate change and its interactions with social vulnerabilities and uneven urban development patterns and processes. This New York City Panel on Climate Change (NPCC) report contributes to the Panel's mandate to advise the city on climate change and provide timely climate risk information that can inform flexible and equitable adaptation pathways that enhance resilience to climate change. This report presents up-to-date scientific information as well as updated sea level rise projections of record. We also present a new methodology related to climate extremes and describe new methods for developing the next generation of climate projections for the New York metropolitan region. Future work by the Panel should compare the temperature and precipitation projections presented in this report with a subset of models to determine the potential impact and relevance of the "hot model" problem. NPCC4 expects to establish new projections-of-record for precipitation and temperature in 2024 based on this comparison and additional analysis. Nevertheless, the temperature and precipitation projections presented in this report may be useful for NYC stakeholders in the interim as they rely on the newest generation of global climate models.

New York City (NYC) faces many challenges in the coming decades due to climate change and its interactions with social vulnerabilities and uneven urban development patterns and processes. This New York City Panel on Climate Change (NPCC) report contributes to the Panel's mandate to advise the city on climate change and provide timely climate risk information that can inform flexible and equitable adaptation pathways that enhance resilience to climate change. This report presents up-to-date scientific information as well as updated sea level rise projections of record. We also present a new methodology related to climate extremes and describe new methods for developing the next generation of climate projections for the New York metropolitan region. Future work by the Panel should compare the temperature and precipitation projections presented in this report with a subset of models to determine the potential impact and relevance of the “hot model” problem. NPCC4 expects to establish new projections-of-record for precipitation and temperature in 2024 based on this comparison and additional analysis. Nevertheless, the temperature and precipitation projections presented in this report may be useful for NYC stakeholders in the interim as they rely on the newest generation of global climate models.

Stochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for nonstationary conditions informed by the global climate model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly pressure change events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multivariable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100‐year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast United States. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the Northeast United States. This SPG provides physically plausible weather ensembles for water resource studies involving climate change.

Increasing the amount of soil organic carbon (SOC) has agronomic benefits and the potential to mitigate climate change. Previous regional predictions of SOC trends under climate change often ignore or do not explicitly consider the effect of crop adaptation (i.e., changing planting dates and varieties). We used the DayCent biogeochemical model to examine the effect of adaptation on SOC for corn and soybean production in the U.S. Corn Belt using climate data from three models. Without adaptation, yields of both corn and soybean tended to decrease and the decomposition of SOC tended to increase leading to a loss of SOC with climate change compared to a baseline scenario with no climate change. With adaptation, the model predicted a substantially higher crop yield. The increase in yields and associated carbon input to the SOC pool counteracted the increased decomposition in the adaptation scenarios, leading to similar SOC stocks under different climate change scenarios. Consequently, we found that crop management adaptation to changing climatic conditions strengthen agroecosystem resistance to SOC loss. However, there are differences spatially in SOC trends. The northern part of the region is likely to gain SOC while the southern part of the region is predicted to lose SOC.

In this study we use three different methodologies to document and compare temperature and precipitation projections for the city of Rio de Janeiro (RJ) over the 21st century. It aims to explore in what way the differences and similarities of those methodologies and their outcomes support the incorporation of climate risks in urban planning and improve effective urban climate change governance. We compared the projections for RJ from the Eta Regional Climate Model from the Brazilian National Institute for Space Research nested in two Hadley Center Global Climate Models (GCMs) (Eta-HadCM3 method and Eta-HadGEM2-ES method) and 33 GCMs from the Coupled Model Intercomparison Project Phase 5 multi-model dataset (Urban Climate Change Research Network - UCCRN method). The three methods showed increasing temperatures for RJ at the end of the century. Precipitation projections span a 13% decrease to a 12% increase when using the UCCRN method or are reduced between 0.4 and 0.5%, when using the Eta-HadGEM2-ES method. However, the middle range of the projections from UCCRN and Eta-HadGEM2-ES is similar. The three methods project an increase of warm days and nights and a decrease of cold days and nights. Nevertheless, although the directions of change are the same applying the three methods, the magnitude differs when considering warm and cold nights. Hence, city stakeholders are better informed when we apply different projection methods as it gives them the opportunity to consider the level of risk they are willing to bear in the future. We observed that defining climate change projections on the city scale based on clear communication and an interactive process between scientists and stakeholders can be used to inform citywide adaptation strategies and sector-specific uses, as well as promote urban climate risk governance.

The New York City Panel on Climate Change (NPCC, 2015) sea level rise projections provide the current scientific basis for New York City scientific decision making and planning, as reflected in, for example, the City's Climate Resiliency Design Guidelines. However, since the IPCC (2013) and NPCC (2015) reports, recent observations show mounting glacier and ice sheet losses leading to rising sea levels. Furthermore, new developments in modeling interactions between oceans, atmosphere, and ice sheets suggest the possibility of a significantly higher global mean sea level rise (GMSLR) by 2100 than previously anticipated, particularly under elevated greenhouse gas emission scenarios. Because of the potentially serious adverse consequences of soaring sea levels to people and infrastructure in low-lying neighborhoods of New York City, we introduce a new high-impact sea level rise scenario, Antarctic Rapid Ice Melt (ARIM), which includes the possibility of Antarctic Ice Sheet destabilization. An earlier “Rapid Ice Melt Scenario” (NPCC, 2010) assumed a late 21st century rate of high-end sea level rise of ∼0.39–0.47 in. per decade, based on paleo-sea level data after the last Ice Age. ARIM represents a new, physically plausible upper-end, low probability (significantly less than 10% likelihood of occurring) scenario for the late 21st century, derived from improved modeling of ice sheet–ocean behavior to supplement the current (NPCC, 2015) sea level rise projections. We briefly summarize key processes that control sea level rise on global to local scales, observed trends, and risks the city faces due to current and ongoing sea level rise. We also briefly recap the NPCC (2015) sea level rise projections for comparison with ARIM. To set the stage for ARIM, we review recent trends in land ice losses (Section 3.5) that reinforce the need to consider such an upper-end scenario. A more detailed discussion of these trends and technical details of the ARIM scenario are provided in Appendix 3.A. Multiple physical processes govern sea level rise on global to local scales. These include: (1) ocean density changes (involving temperature and salinity); (2) changes in ocean currents and circulation patterns; (3) ice mass losses from glaciers, ice caps, and ice sheets; (4) redistribution of ocean water in response to changes in the Earth's gravitation, rotation, and deformation caused by current ice mass losses (collectively referred to as “fingerprints”); (5) past ice mass losses (i.e., glacial isostatic adjustments, GIA1); (6) other vertical land movements caused by ongoing tectonic activity, sediment compaction due to loading, and subsurface extraction of water, oil, gas; and (7) changes in land water storage, for example, in dams or from groundwater mining. Thermal expansion along with losses of ice from mountain glaciers and small ice caps have historically been the major contributors to observed mean global sea level rise, but in recent decades, shrinking ice sheets have played a growing role and dominate in the higher scenarios for future GMSLR (Slangen et al., 2017, 2016; Kopp et al., 2014; Church et al., 2013, IPCC AR5). These processes interact in ways that differ from place to place, such that for any given locality the sum of the components for local sea level rise may deviate significantly from the global mean. New York City lies in a region that experiences higher than average sea level rise due to enhanced thermal expansion, mounting ice losses from the Antarctic Ice Sheet, and GIA. An additional possible factor is changes in ocean circulation. A major oceanic circulation system, the Atlantic Meridional Overturning Circulation (AMOC), could slow down due to decreased North Atlantic salinity resulting from Greenland ice losses, increased precipitation and northern river freshwater inflow, and sea ice attrition. The resulting heat build-up due to a weakened North Atlantic circulation would increase thermal expansion and redistribute water mass shoreward especially in the mid-Atlantic region, including New York City (Krasting et al., 2016; Yin and Goddard, 2013; Yin et al., 2010; 2009). While a regional sea level acceleration “hotspot” has been observed in tide gauge records along the Atlantic coast from Cape Cod to Cape Hatteras (including New York City) since the early 1990s (Sallenger et al., 2012; Boon, 2012), it is more likely that this hotspot reflects high interannual to multidecadal ocean variability than a shift in ocean circulation (Kopp, 2013; Valle-Levinson et al., 2017). Attribution of the hotspot to a weaker Gulf Stream and slowdown of the AMOC (Rahmstorf et al., 2015; Yin and Goddard, 2013) has not yet been substantiated (Böning et al., 2016; Watson et al., 2016) and is thus premature. However, this process could become important in the future (e.g., Section 3.4.2). In addition––perhaps counterintuitively, given the great distance between Antarctica and New York City––ice losses from Antarctica are amplified along the mid-Atlantic coast by the gravitational responses to this change. As the mass of the ice sheet shrinks, its gravitational attraction weakens, and water congregates farther from it. This, as well as continued GIA-related land subsidence, leads to a higher than average local sea level rise. On the other hand, gravitational effects from more nearby ice losses on Greenland and northern hemisphere glaciers mean that these ice losses raise local sea levels less than the global average. The net effect of all these processes drives New York City sea level rise above the global average (e.g., Carson et al., 2016; Slangen et al., 2014; Kopp et al., 2014.; Horton et al., 2015a). Sea level rise represents one of the most momentous consequences of climate change, potentially affecting hundreds of millions of people worldwide. In recent decades, melting ice sheets and glaciers account for over half of the total observed current rise (Dieng et al., 2017; Rietbroek et al., 2016), a fraction likely to increase with continued global warming (see Sections 3.4.1, 3.5, and 3.6). This section briefly reviews current global and local/regional trends in sea level rise, to provide context for future sea level changes, discussed in later sections. Tide gauge-based reconstructions of GMSLR between 1900 and 1990 range between 0.04 and 0.08 in./year (1 and 2 mm/year) (Dangendorf et al., 2017; Jevrejeva et al., 2017; 2014; Hay et al., 2015; Church et al., 2013; Church and White, 2011). Between 1993 and 2017, satellite altimetry shows an average GMSLR of around 0.12 in./year (3 mm/year),2 after accounting for satellite instrumental drift that affected the earlier TOPEX/Poseidon mission between 1993 and 1998 (Watson et al., 2015; Dieng et al., 2017; Beckley et al., 2017). After further accounting for the effects of the 1991 Mt. Pinatubo volcanic eruption and of strong El Niño–Southern Oscillation events, these revised estimates show clear acceleration of the sea level record, attributable to accelerated ice sheet mass loss (Chen et al., 2017; Dieng et al., 2017; Nerem et al., 2018; Fig. 3.1). Glaciers, ice caps, and ice sheets combined have raised ocean levels by 0.01 in./year (0.31 mm/year) between 1992 and 1996, increasing to 0.07 in./year (1.85 mm/year) between 2012 and 2016 (Bamber et al., 2018). Furthermore, GMSLR since the late-19th century has greatly exceeded the range of variability seen over the last three millennia (Kopp et al., 2016; Gehrels and Woodworth, 2013). These results imply two stages of global mean sea level acceleration: the first between late 19th and early 20th century to around 1990, which may in part reflect natural climate cycles, and the second from the 1990s to the present. Since 1970, anthropogenic factors may account for over 70% of the rise (Slangen et al., 2016). The local or relative3 sea level rise in New York City has averaged 0.11 in./year from 1850 to 2017 as measured by The Battery tide gauge, nearly double the 1900–1990 mean global rate (Fig. 3.2; NOAA, 2017). Local GIA-related subsidence, which accounts for roughly half of the observed relative sea level rise (Engelhart et al., 2011; Engelhart and Horton, 2012), is a key reason why New York City's rate of sea level rise is so high. As elsewhere, the historic New York City trend has increased markedly relative to the previous millennium (Kemp et al., 2017). Sea level rise has been tracked over time by the NPCC using data from both tide gauges and satellite altimetry. (For more information on tide gauges, see the NOAA Tides & Currents4 website; for satellite altimetry, see NASA Jason5 and AVISO/CNEs6 websites). New observations from these sources are included in updated analyses of sea level rise trends in each NPCC report and are included in reference to any new projected values. NPCC 2019 extends the observed record for sea level rise from NPCC 2015. In addition, NPCC 2019 analyzed how the trends in recent sea level rise compare in general to the projected changes in sea level from NPCC 2015 into the 2020s timeslice, which encompasses the time period from 2020 to 2029. Figure 3.3 shows the observed trend in sea level rise at The Battery in New York City from 1900 through 2017 compared to the NPCC 2015 projections. While NPCC3 cannot yet compare analytically projected to observed values from NPCC 2015 through 2017–2018 since we have not yet entered the onset of the 2020s time slice, nevertheless, the most recent observed trends show that sea level at The Battery has continued its upward rise since the previous NPCC report. A more comprehensive, comparative analysis will be part of the next NPCC report when a greater overlap will exist between the observed trend time period and projected values from NPCC 2015. However, such comparisons should be viewed with caution because of the role of natural variability in the short term. New York City is part of a metropolitan region (population 23.7 million7) that covers three adjacent states—Connecticut, New York, and New Jersey. Long-term sea level rise, as well as episodic coastal flooding, poses a high risk to the population, housing, and many essential New York City infrastructure facilities that line the 520 miles (837 km) of the city's waterfront. These include three major international airports, shipping infrastructure, segments of commuter and intercity bus and rail transit systems, many subway, tunnel, and bridge entrances, nearly all city wastewater treatment plants (WWTPs), oil tanks and refineries, most power plants, and telecommunication networks. The combined effects of New York City sea level rise (18 in., 45.7 cm between 1856 and 2017; Fig. 3.2, NOAA, 2017) and changes in storm climate variability (Orton et al., 2016; Lin et al., 2016; Reed et al., 2015; Talke et al., 2014; and Wahl et al., 2017) have increased the impact of coastal flood hazards (see also, Chapter 5: Mapping Climate Risk). Due to historic growth patterns and high-density shoreline development, a significant population resides within areas exposed to coastal hazards. The above-average water levels during strong hurricanes or hybrid storms, such as Sandy (Oct., 2012), Donna (Sept., 1960), Irene (Aug., 2011), and the unnamed 1788 and 1821 hurricanes, as well as “nor'easters” (e.g., Dec, 1992) resulted in substantial coastal flooding (Section 3.2.1). The location of property and key infrastructure near the shore or within the FEMA 1%-annual-chance floodplain places them at increased risk to ongoing and future sea level rise, in the absence of protective structures, such as levees, or other adaptation strategies. For example, storm surges occurring on top of higher sea level can damage wastewater treatment facilities, causing combined sewer overflows and pollution of waterways (NYC Hazard Mitigation Plan 2014). Acutely aware of this hazard, especially following Hurricane Sandy, the New York City Department of Environmental Protection has taken steps to increase resiliency and minimize potential damages. Buildings damaged by severe coastal erosion, prolonged saltwater exposure, and/or tidal flooding in low-lying areas require costly retro-fitting or even eventual relocation. In addition to high coastal storm floods and heavy rain, rising sea level is currently causing sewer surcharge and flooding streets farther away from the coast. The probability of blocked outfalls caused by poor drainage and additional backflow increases with elevated coastal storm surge superimposed on rising sea levels. Historical sea level rise in New York City (Fig. 3.2) has intensified the effects of coastal storm floods (Talke et al., 2014). In the lowest lying neighborhoods, flooding now occurs at times of high astronomical tides (tidal flooding), even in the absence of active storms. The frequency of such so-called “nuisance flooding” at The Battery has more than doubled since the 1950s (Sweet and Park, 2014; Strauss et al., 2016). Sea level rise alone will increase the severity and occurrence of New York City coastal storm-driven flooding, irrespective of changes in storm characteristics (Buchanan et al., 2017; Lin et al., 2016; Orton et al., 2016; Reed et al., 2015; Talke et al., 2014; Kemp and Horton, 2013). Further discussion on historic, current, and future flood risks is given in Chapter 4: Coastal Flooding. The Mississippi Delta and Chesapeake Bay are examples of areas already experiencing permanent land inundation due to high rates of relative sea level rise from land subsidence superimposed on global sea level rise. The Chesapeake Bay area has the highest rates of relative sea level rise on the East Coast, due to GIA and groundwater withdrawal (Eggleston and Pope, 2013), which has led to the shrinkage or loss of several small islands (Gornitz, 2013). The high relative sea level rise has led to increased tidal flooding in places such as Norfolk, Virginia (see also discussion of tidal flooding in Chapter 4). Although New York City is not at immediate risk of extensive land inundation, the regions currently experiencing inundation provide a preview of potential permanent land loss due to sea level rise facing some New York City neighborhoods under the ARIM scenario in the later years of the 21st century (see Chapter 5, Fig. 5.1). The first areas that could be affected include low-lying city neighborhoods that will experience frequent tidal flooding and, in a few cases, permanent inundation by the 2050s and especially the 2080s (e.g., compare Fig. 5.1 with Figs. 5.2 and 4.4). (Note: Because the ARIM scenario shown in these figures is based on data with high associated uncertainties, it should be regarded as suggestive of areas that might become inundated and should therefore not be used for planning purposes. See further discussion and disclaimer in Chapter 4: Coastal Flooding, and Chapter 5: Mapping Climate Risk). Studies show that intertidal salt marshes and particularly their substrate play an important role in attenuating storm waves as they break on shore (Marsooli et al., 2017), although they may not lessen high storm water levels, or reduce flooding if deep shipping channels are present (Orton et al., 2015). Many New York City salt marshes, including in Jamaica Bay, have receded historically and have become increasingly ponded, with enlarging tidal inlets and pools (Hartig et al., 2002). In addition to historic sea level rise, other stressors have led to attrition of local salt marshes, such as channelization, shoreline development and armoring with engineered structures, excess nitrogen nutrient loading from nearby sewage treatment plants, and inadequate sediment supply (e.g., Hartig et al., 2002). As a result, salt marshes at the shoreline edge are converting to tidal mudflats. The National Park Service, in conjunction with the U.S. Army Corps of Engineers, is engaged in restoration efforts at several Jamaica Bay salt marshes (e.g., Elders Point Marsh, Yellow Bar Hassock, and Rulers Bar). Rising sea levels lead to longer periods of salt marsh submergence during high tides. Salt marsh vegetation zones can gradually shift landward, but may not find space, due to urban development (i.e., “coastal squeeze”) or too steep a rise in inland topography. Wetlands will drown in place wherever rates of accretion cannot keep pace with sea level rise, and/or if sediment supplies are insufficient. However, as noted above, sea level rise is just one of many environmental factors that contribute to New York City saltmarsh losses. Sea level rise, in addition to climate change, can alter the flow of saltwater and propagation of tide and storm surge up streams, in estuaries such as the Hudson River, and into coastal lagoons. The mean location of the salt front pushes upstream as a result. Hydroclimate also influences the position of the saltwater front in the Hudson River. A decrease in precipitation reduces streamflow, which allows the salt front to migrate further upstream (and vice-versa); higher temperatures increase evaporation and decrease freshwater runoff, also forcing an upstream migration of the salt front (Buonaiuto et al., 2011). Salt front migration up the Hudson River (and the Delaware River Basin; Chapter 2, Climate Science) during severe droughts and/or higher sea levels could adversely impact the emergency New York City drinking water supply from the Hudson River at the Chelsea Pumping Station. Sea level rise will also increase the salinity of brackish water in the estuary and lagoons, also affecting inflow of seawater to sewers and WWTPs located along the saltwater-dominated coastline and thereby lessen infiltration efficiency. In addition, higher water levels will reduce the capacity of WWTP effluents to drain by gravity and pumping (see also Chapter 4, Coastal Flooding). Although less urgent today, salinization accompanying sea level rise may become a major issue for drainage systems and warrants further investigation. Structures not designed for exposure to repetitive and lengthening saltwater exposure would also face more frequent and higher repair or replacement costs (Solecki et al., 2015). Sea level rise, in conjunction with higher waves and/or water levels during intense storms, such as Hurricane Sandy in 2012, is likely to exacerbate ongoing coastal erosion, particularly of exposed, ocean-facing shorelines. This can disrupt sediment transport and undermine natural landforms, like beaches and salt marshes offering protective features, with associated land loss and environmental degradation. In urban areas, coastal erosion and flooding can severely damage structures, and if unchecked, can undermine foundations, ultimately leading to building collapse, as shown during Hurricane Sandy for the New Jersey and New York regions (Hatzikyriakou et al., 2016; Hatzikyriakou and Lin, 2018). An integrated approach for managing high erosion risks includes upgrading major structural protections, such as seawalls, revetments, bulkheads, groins, etc., as well as implementing beach nourishment and living shorelines. Continual erosion of the city's sandy beaches requires periodic nourishment with sand dredged from offshore (New York City, 2014). Potential coastal restoration projects by the U.S. Army Corps of Engineers are in review for Coney Island and the Rockaways (USACE, 2016a, 2016b). Coastal erosion risks can also be mitigated by the limitation of high-density development in high-erosion hazard zones. Three current “erosion hotspot” neighborhoods (south shore of Staten Island, Coney Island, and Rockaway Peninsula) have been designated Coastal Erosion Hazard Areas (CEHA), for which new construction or land use change requires special permits from the New York State Department of Environmental Conservation (NYC, 2014). As atmospheric greenhouse gases continue to accumulate, and temperatures climb, sea level is expected to rise in the future at accelerating rates. Climate scientists look ahead by using computer-generated coupled global atmospheric-oceanographic models that are based on known laws of physics that govern our climate. Section 3.4.1 briefly reviews the sea level rise projections of the Intergovernmental Panel on Climate Change (IPCC, 2013), and several newer reports that suggest a higher future global sea level than that in the IPCC report. The sea level rise projections for New York City developed by NPCC (2015), which are reaffirmed for use as the basis of New York City resiliency planning, are described in Section 3.4.2. The IPCC AR5 (Church et al., 2013) projects future climate changes for a set of four representative concentration pathway (RCP) scenarios, which represent different trajectories of greenhouse gas emissions, aerosols, and land use/land cover (Moss et al., 2010). They range from a high greenhouse gas emission “business-as-usual” scenario (RCP8.5) to one involving strong mitigation efforts (RCP2.6). Driven by the RCPs, a suite of coupled atmospheric and oceanographic global climate models (AOGCMs) numerically simulate physical interactions between the atmosphere, ocean, continents, and sea ice, in order to project future trends in climate variables including temperature, precipitation, and sea level rise. AOGCMs directly compute changes in ocean density (temperature and salinity) and circulation patterns. Temperature and precipitation projections from AOGCMs are used to drive separate numerical models to estimate surface mass balance8 of glaciers and ice sheets. Models that include both dynamic ice flow and surface mass balance driven by climate projections (i.e., temperature, precipitation) estimate future changes in discharge of ice past the grounding line9 and calving rates of icebergs. The individual components are then summed to obtain global sea level. An alternative approach to projecting GMSLR, the semiempirical approach, makes projections of future sea level rise based on the assumption that the statistical relationship that existed between past temperatures and rates of sea level change will continue into the future. Thus, the future trajectory of sea level rise remains closely linked to that of increasing global temperature (e.g., Moore et al., 2013; Rahmstorf et al., 2012, Rahmstorf, S., 2007; Kopp et al., 2016). However, this assumption may no longer hold if processes that were minor contributors to past sea level change, such as ice sheet dynamics, become major contributors in the future. IPCC (2013) projects a “likely”10 GMSLR by 2100 of 0.9–2.0 ft for RCP2.6, 1.2–2.3 ft for RCP4.5, and 1.7–3.2 ft for RCP8.5 relative to a 1986–2005 baseline, and notes the potential for collapse of marine-based parts of the Antarctic Ice Sheet to contribute another several tenths of a meter (Church et al., 2013), IPCC, 2013, Chapter 13, Table 13.5). An earlier assessment (Pfeffer et al., 2008) suggested that 6.6 ft was a physically plausible upper bound to GMSLR, a level adopted by the Third National Climate Assessment for its highest sea level rise scenario (Parris et al., 2012). However, this upper bound was subsequently criticized (Miller et al., 2013) for failing to fully represent uncertainty regarding Antarctica (Bamber and Aspinall, 2013), thermal expansion (Sriver et al., 2012), and land water storage (IPCC, 2013). Since IPCC (2013), new observations from the Greenland and Antarctic Ice Sheets (e.g., Rignot et al., 2014), progress in ice sheet–ice shelf–ocean modeling (e.g., Joughin et al., 2014), and expert assessments (Horton et al., 2014; Bamber and Aspinall, 2013) have reaffirmed the physical plausibility of sea level rise well in excess of the IPCC (2013) “likely” range (Jevrejeva et al., 2014; Kopp et al., 2014; Slangen et al., 2017). Newly recognized mechanisms for ice-shelf instability further emphasize the plausibility of high-end outcomes, especially beyond 2100 in high-emission futures (Pollard et al., 2015; DeConto and Pollard, 2016; Kopp et al., 2017; Le Bars et al., 2017; Wong et al., 2017; see also, Section 3.4.2). Based on these findings, the Fourth National Climate Assessment recommended a suite of GMSLR scenarios for the period 2000–2100 that range between a “low” scenario of 1.0 ft to a physically plausible “extreme” scenario of 8.2 ft by 2100 (Sweet et al., 2017). Sweet et al. (2017) additionally describe methods for adapting these projections to regional scales, as illustrated for New York City in in Section 3.4.2. and Appendix 3.A. Although many future global sea level rise projections end in 2100, the longevity of atmospheric CO2 commits us to higher temperatures and sea level long after reduction of stabilization of greenhouse gas emissions. Ending further emissions by mid-century would allow some of the anthropogenic CO2 and temperature to slowly diminish after several decades, with gradual dissipation of the balance. It would take centuries to millennia to reach a new equilibrium state. In the interim, sea level will continue to rise well beyond 2100, because of the continued climate warming and slow heat penetration into the deep ocean (Clark et al., 2016; Mengel et al., 2016; Golledge et al., 2015). In its second report, the NPCC (2015) developed a multicomponent methodology for projecting future sea level rise for New York City (Horton et al., 2015a). Components include oceanographic changes (thermal expansion, dynamic ocean height), ice mass losses with associated gravitational and glacial isostatic adjustments, and anthropogenic land water storage change, for an ensemble of 24 CMIP global climate models and two climate change scenarios (RCP4.5, RCP8.5), as well as literature review and expert judgment. Sea level rise, relative to the 2000–2004 base period, was calculated for the 10th, 25th, 75th, and 90th percentiles from a model-based distribution and estimated ranges from the literature. NPCC (2015) assumed that all uncertainties were perfectly correlated so that, for example, the 90th percentile projection combined the 90th percentile values for each of the different terms. While this could lead to overly high estimates, NPCC (2015) offered some leeway in case the individual component projections—consistent with most sea level rise projections in recent decades—would later be found to underestimate the extreme tail of the distribution. NPCC (2015) projects a mid-range (25th–75th percentile) sea level rise of 11–21 in. (0.28–0.53 m) at The Battery by the 2050s and 18–39 in. (0.46–0.99 m) by the 2080s, relative to a 2000–2004 baseline. High-end estimates (90th percentile) reach 30 in. (0.76 m) by the 2050s, 58 in. (1.47 m) by the 2080s, and 75 in. (1.91 m) by 2100 (Table 3.1). Appendix 3.B illustrates how recent observed trends in sea level rise from 1900 to 2017 compare to these projected changes from NPCC (2015). The results of a similar study by Kopp et al. (2014), which did not assume perfect correlation of uncertainties, and applied a hybrid approach to ice sheets that blended NPCC (Horton et al., 2015a) and IPCC methodologies, are shown in Appendix Table 3.A.1. Results from a more recent study (Kopp et al., 2017), incorporating Antarctic ice-sheet projections from DeConto and Pollard (2016), and projections based on these studies, are also shown in Appendix Table 3.A.1. Appendix Table 3.A.2 places these projections in the context of the local sea level rise scenarios developed by Sweet et al. (2017) for the Fourth National Climate Assessment (see Appendix 3.A for more details). As mentioned in Section 3.1, sea level rise in New York City is expected to exceed global mean values (NPCC, 2015; Carson et al., 2016; Kopp et al., 2014; Love et al., 2016). This arises primarily because of GIA-related subsidence, far-field effects of Antarctic ice loss, and above-average ocean dynamic height due to projected slowdown of the AMOC with continued ocean freshening and Greenland ice losses (Yin and Goddard, 2013; Yin et al., 2010; 2009). Enhanced warming in the western Atlantic relative to the Pacific Ocean may also elevate steric sea level rise along the East Coast, particularly for high carbon emission scenarios (Krasting et al., 2016). Although gravitational effects associated with proximity to Greenland and northern hemisphere glaciers will partially reduce sea level rise, the combined effect of all contributing factors will result in higher than average sea level rise for New York City (Sweet et al., 2017; Love et al., 2016). It should be re-emphasized that the NPCC (2015) sea level rise projections represent the current scientific foundation for New York City decision making and planning. However, recent observed trends in land ice mass losses and advances in ice–ocean–atmosphere interactions raise the possibility of higher future sea levels than previously assumed (Section 3.5). Furthermore, NPCC (2015) sea level rise estimates lie within the 10–90% probability range. They do not provide sea level rise values with a lower than 10% probability of occurrence by 2100 (i.e., the very large sea level increases that lie in the upper 10% tail of the sea level rise probability distribution). Nevertheless, consideration of such high-end sea level rise outcomes is of great importance for effective long-term decision making. Focusing on the central range may lead to underestimation of the future risks, especially in the light of science that suggests that high-end scenarios may become more probable under high-emissions scenarios than thought a few years ago. A new upper-end, low-probability sea level rise scenario, introduced in Section 3.6, is designed to address the concerns of stakeholders interested in long-term planning, who may need to examine credible scenarios at the extreme upper tail of the distribution. The ARIM scenario provides one physically plausible, low-probability scenario (i.e., one with significantly less than 10% likelihood of occurrence by 2100) for considering the consequences of very unlikely, yet high-impact outcomes. For example, many public or private sector dec

The focus of NPCC3 is on high-risk events involving extreme temperatures, extreme precipitation, and drought. Current trends are presented using historical climate records of high temperature, cold snaps, humidity, and extreme precipitation for the New York metropolitan region. The geographical span of the New York metropolitan region considered here includes, in addition to New York City, adjacent sections of New Jersey such as Newark, Jersey City and Elizabeth, as well as other nearby locations in New York such as Yonkers and Long Island. Historical records of droughts in the Delaware watershed region are also examined. Each climate extreme is analyzed for detection of current trends, and future projections are updated for high-temperature extremes as a test of new methods that could be utilized by NPCC4. These represent finer temporal and spatial resolutions that may be of practical use to key stakeholders in New York City for planning purposes and/or emergency responses. They include local projections of extreme heat and demonstrate the role of the heterogeneous landscape of the city in each process (e.g., how the urban heat island (UHI) affects city neighborhoods differently). Each section of the chapter presents definitions, baselines, methods, and projections, along with uncertainties and recommendations for future work. As in NPCC2, NPCC3 makes use of definitions, measurements, baselines, and scenarios to represent how the probabilities of climate events may change in the future. Here, the focus is on extreme events. For most climate hazards, the definitions of extremes are consistent with the NPCC2, specifically for extreme heat, cold spells, and precipitation. NPCC3 confirms the temperature and precipitation projections of NPCC2 as those of record for use in planning. Based on emerging science, NPCC3 introduces a new methodology for analyzing heat and precipitation extremes that could be used for developing future projections of record in NPCC4. In NPCC2, temperature analyses included projections of average temperature changes and changes in heat waves and hot days. NPCC3 explores new methodologies for downscaling heat extremes and introduces new metrics to analyze historical and projected humidity. For precipitation, NPCC2 developed quantitative projections for average rainfall and daily maximum rainfall events, and NPCC3 introduces a methodology for quantifying projections for sub-daily heavy downpour rain events. In addition, NPCC3 examined how current observations of temperature and precipitation changes compare to projected changes from NPCC2 into the 2020s time slice, which encompassed the time period from 2010 to 2039. Figure 2.1 shows the results of this analysis and demonstrates that observations from 2010 to 2017—the period for which both observed data and NPCC2 projections are available to compare—have been largely consistent with projected changes in average conditions for both temperature (Fig. 2.1a) and precipitation (Fig. 2.1b). However, these comparisons should be viewed with caution because of the role that natural variation plays in the short term. As NPCC3 shifts from a focus on average conditions to extremes, the baselines in some cases vary according to the relevance of the period for the extreme event researched and the period for which data are available. To the extent possible, consistency with NPCC2 is maintained. For example, the baseline for heat waves is 1971–2000, which is the same as NPCC2. However, NPCC3 uses summer months for extreme heat events (June, July, and August) for three reference weather stations, while NPCC2 used the whole year with one reference weather station. NPCC3 uses bias-corrected statistical downscaling and develops future projections for extreme heat based on summer seasons only and includes high-resolution dynamical downscaling at 1 km for selected time slices. Summer humidity is included in the projections as a new heat-related variable. The section of extreme temperatures closes with a short view of cold spells and winter extremes. The section on urban flooding makes use of shorter, more detailed records of satellite and radar data to demonstrate the spatial distribution of these extreme events at sub-hourly time resolution. For droughts, a much longer precipitation record based on tree rings is used to capture decadal variations in the New York City watershed region, and reconstructions of inflows to reservoirs are used to understand how frequently extreme droughts have occurred in the past. To create the new extreme event projections, bias-corrected statistical downscaling is used (see Section 2.3). In the Appendix, we provide an example of dynamic downscaling, a method that can capture the role of the urban built environment in magnifying heat events and mitigating flooding events. Model outputs from the fifth phase of the Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012) are used for projections of extreme heat. Methods for calculating future projections are consistent with NPCC2 but are updated to account for climate model biases in simulating the distribution of temperature (National Climate Assessment; Walsh et al., 2014). Results are provided in 30-year intervals centered on the 2020s, 2050s, and 2080s as defined by NPCC2. The ensemble of CMIP5 results includes two representative concentration pathways (RCPs) (see Box 2.1). Summer (defined as the months of June, July, and August) temperatures are expected to increase in New York City throughout the 21st century (Horton et al., 2015), leading to more frequent and intense extreme heat events known as heat waves. Here, we follow the definition of heat waves according to the National Weather Service (NWS), that is, an interval of 3 (or more) consecutive days with temperatures of at least 90 °F (32.22 °C). Heat waves affect a wide range of human activities. These effects include increasing energy demand (Schaeffer et al., 2012; Sailor, 2001; Santamouris, 2014) and mortality (Knowlton et al., 2007; Luber and McGeehin, 2008; Anderson and Bell, 2010; Rosenthal et al., 2014). Moreover, higher temperatures associated with urbanization, a phenomenon called the Urban Heat Island (UHI) (Oke, 1982), exacerbate the impacts of extreme heat events (Li and Bou-Zeid, 2013; Ramamurthy and Bou-Zeid, 2016; Ramamurthy et al., 2017; Ortiz et al., 2018). New York City, being the most populated urban area in the United States with over 8 million people (U.S. Census Bureau, 2018), has a large human and economic incentive to understand and mitigate the negative impacts of these events now and in the future. Extreme heat projections have primarily been developed on global (Meehl and Tebaldi, 2004) or continental scales (Gao et al., 2012), with less work focusing on local urban projections that require accounting for finer-scale processes and feedbacks that may affect the occurrence and characteristics of high-temperature events. An example of these processes is the soil moisture-heat wave feedback, wherein dry soil conditions may amplify heat waves by reducing available moisture for evaporative cooling (Seneviratne et al., 2006; Lorenz et al., 2010; Fischer et al., 2007). Cities may amplify these feedbacks by reducing exposed soil area, greatly reducing the capacity for water retention near the land surface (Li and Bou-Zeid, 2013; Ramamurthy and Bou-Zeid, 2016; Ramamurthy et al., 2017). Other relevant city-scale processes include waste heat from buildings and transportation (Taha, 1997; Ichinose et al., 1999; Offerle et al., 2005), lower surface reflectivity of built surfaces (Taha et al., 1988; Morini et al., 2016; Ramamurthy et al., 2015) and increased heat storage in buildings and built structures (Oke et al., 1981; Arnfield and Grimmond, 1998). Humidity content of the atmosphere can play an adverse role in how humans react to high heat conditions (Davis et al., 2016; Hass et al., 2016). As air becomes more saturated with water vapor, the human body becomes less able to shed excess heat through evaporative cooling of perspiration. This can lead to exacerbation of high-temperature impacts such as fatigue and heat exhaustion. This section presents extreme heat and specific humidity projections for New York City using new methods, accounting where possible for urban effects via statistical processing of global climate model (GCM) simulation data. This statistical processing, or downscaling, is necessary because global models have, in general, very coarse spatial resolution (>100 km2) and are thus not able to resolve coastlines, topography, and land cover. The downscaling technique used by NPCC3 is histogram matching. It aims to adjust the model representations of observed climate by correcting their mean and variance to match a representative set of observations in the target domain (see Appendix 2.B). This differs from the bias adjustment procedure of NPCC2 that combined GCM results with station records to downscale the projections to the New York metropolitan region using the "delta method" (Horton et al., 2015), where mean monthly projected changes are applied to daily observations. In NPCC3, as in NPCC2, the climate projections are based on multiple climate models, driven by two RCPs—RCP4.5 (referred to as medium emissions) and RCP8.5 (referred to as high emissions) (see Box 2.1). The aim of this approach is to capture the uncertainties emerging from the range of model results as well as those related to the impacts of future industrial activity, energy use, and technology on greenhouse gases (GHGs), aerosol emissions, and land use change. For consistency, NPCC3 uses the same baseline period as the NPCC2 (1971–2000). Definitions and methods are detailed in Table 2.1. Climate change refers to a significant change in the state of the climate that can be identified from changes in the average state or the variability of weather and that persists for an extended time period, typically decades to centuries or longer. Climate change can refer to the effects of (1) persistent anthropogenic or human-caused changes in the composition of the atmosphere and/or land use, or (2) natural processes such as volcanic eruptions and Earth's orbital variations (IPCC, 2013). A GCM is a mathematical representation of the behavior of the Earth's climate system over time that can be used to estimate its sensitivity to atmospheric concentrations of greenhouse gases (GHGs), aerosols, and land use change. Each model simulates physical exchanges among the ocean, atmosphere, land, and ice. RCPs are sets of trajectories of concentrations of GHGs, aerosols, and land-use changes developed for climate models as a basis for long-term and near-term climate-modeling experiments (Moss et al., 2010). RCPs describe different climate futures based on different amounts of climate forcings. These data are used as inputs to GCMs to project the effects of these drivers on future climate. The NPCC uses sets of GCM simulations driven by two RCPs, known as 4.5 and 8.5. The set of GCM simulations driven by RCP 4.5 is defined here as a medium-emissions scenario, and that by RCP 8.5 as a high-emissions scenario. On the basis of the selection of the RCPs and GCM simulations, local climate change information is developed for key climate variables—temperature, precipitation, and associated extreme events. These results and projections reflect a range of potential outcomes for the New York metropolitan region. A climate hazard is a weather or climate state such as a heat wave, flood, high wind, heavy rain, ice, snow, or drought that can cause harm and damage to people, property, infrastructure, land, and ecosystems. Climate hazards can be expressed in quantified measures, such as flood height in feet, wind speed in miles per hour, and inches of rain, ice, or snowfall that are reached or exceeded in a given period of time. Uncertainty denotes a state of incomplete knowledge that results from lack of information, natural variability in the measured phenomenon, instrumental and modeling errors, and/or from disagreement about what is known or knowable (IPCC, 2013). Historical trends of daily maximum summer temperature in New York were analyzed using Central Park weather station, John F. Kennedy (JFK), and LaGuardia Airports during June, July, and August (Fig. 2.2). Central Park has the longest historical record, dating back to 1900, where the average annual daily maximum summer temperature has been rising at an average of 0.2 °F per decade from 1900 to 2013. JFK and LaGuardia weather stations go back to 1970, where average annual daily maximum summer temperatures have been increasing at a rate of 0.5 °F per decade and 0.7 °F per decade, respectively. The distance between these weather stations provide insights into processes that affect temperatures near the surface, such as sea breezes1 and the UHI. Sea breeze effects appear in stations located close to Long Island's southern shore (e.g., JFK), with lower daily maximum temperatures compared to their in-land counterparts (Fig. 2.2). Sea breeze impacts on temperatures show that geospatial heterogeneity of the urban landscape plays a role in near-surface temperatures, and therefore impact occurrences of extreme heat. The weather station located at JFK, which experiences afternoon sea breezes, has a mean summer maximum temperature of 80.6 °F, whereas the other stations have a mean value of 82.7 °F, which is 2.1 °F higher. This is consistent with climatological studies (e.g., Gedzelman et al., 2003) of the UHI in the region, which have found that afternoon summer sea breezes may shift the center of the urban heat island west and north, toward New Jersey and The Bronx. Heat wave characteristics considered here are their frequency (events/year), mean event duration (number of days/event), and intensity (average maximum temperature/heat wave). NPCC2 had previously analyzed frequency and mean event duration; heat wave intensity is a new metric in NPCC3. While a new methodology is tested here that is different from NPCC2, NPCC3 confirms the use of NPCC2 projections as the projections of record for New York City to plan for extreme heat. The new methodologies presented in NPCC3 could be used in developing new projections of record in NPCC4. Using a composite observed temperature record derived by averaging the daily maximum temperature over the three New York City stations, results from 26 GCMs were bias corrected in order to project distributions of heat waves for the NPCC3 time slices following the methods of Piani et al. (2010) and Hawkins et al. (2013). (See Appendix 2.C for detailed methods.) The mean and standard deviation of a given variable were used to adjust the model distribution against the target observed distribution. For each GCM, the closest land grid point was selected, as was done in NPCC2, and the distribution of maximum daily temperature at this point was bias corrected against the city's composite maximum temperatures. This method is referred to as a "single-point" bias correction. The previous NPCC2 approach may have resulted in a bias toward slightly cooler projected extreme temperatures compared to those projected using the NPCC3 bias-correction methods, particularly toward the warmer periods in the 2080s time slice. These changes may be due to the correction to the variance that, at least partially, addresses the fact that GCM grid boxes near coasts may include water. NPCC3 analysis of the bias-corrected single-point projections shows overall increase across all heat wave metrics throughout the 21st century (Fig. 2.3). To highlight the sensitivity to emission scenarios, we present the response to medium-emission and high-emission scenarios separately. In Table 2.2, the projections are based on the distribution of multimodel results showing the 10th, 25th, 75th, and 90th percentile outcomes across both RCP scenarios, as was done in NPCC2. Mean daily maximum temperature (Fig. 2.3a) shows a nearly linear trend in the high-emissions scenario (RCP8.5), whereas the rate of change in the medium-emissions scenario (RCP4.5) slows after 2040. The number of heat waves per year (Fig. 2.3b) shows far less deviation between the two emissions scenarios. Both scenarios increase at a pace of about one additional yearly event every 20 years until 2060, where growth slows down considerably. This may be due to consecutive events coalescing into very long heat waves, which becomes more likely as heat waves increase in length and frequency. It is also an artifact of the definition of heat wave used, which establishes an unchanging temperature threshold through the entire century. As mean temperatures increase, meeting the 90 °F on consecutive days becomes more likely. Uncertainty in projections as described by confidence intervals increase over time, with a spread of 1 event in the first half of the century that grows to a spread of about two events by end of century. Mean event duration projections (Fig. 2.3c) are similar across the scenarios in the first half of the century, growing by around 2 days per 20-year period. However, the high-emissions scenario projections show accelerated growth in the latter half of the century, as well as more spread in the model ensemble, with an uncertainty band spanning about 10 days, compared to about 2 days in the first half. This accelerated increase in event duration may explain the stabilization of event frequency projections in Figure 2.3(b), as events may aggregate into longer heat waves. Mean intensity, defined as the mean of event maximum temperatures, shows large interannual variation (Fig. 2.3d), with projected values that increase from about 93 °F early in the century, to 95–98 °F by the end of the century. Confidence interval bands increase slightly throughout by end of century, reaching an ensemble spread of about 1 °F. Additional key metrics of extreme heat explored are number of days above 90 and 100 °F in the summer season (Table 2.2). Projected days above 90 °F are expected to become more likely as summer temperatures increase. By the 2080s, projections show 24 (10th percentile) to 75 (90th percentile) days above 90 °F compared to the 1971–2000 baseline (10 days).2 The humidity content of the atmosphere can play an adverse role in how humans react to high heat conditions (Davis et al., 2016; Hass et al., 2016). We present projections of daily mean specific humidity based on a 26 multimodel ensemble, across medium- and high-emissions scenarios, as in Section 2.3.2. For each model, the land grid point closest to New York City is used. Due to a lack of specific humidity records from all weather stations, GCM humidity was bias corrected based on LaGuardia Airport only. In addition, the 1971–2000 baseline for specific humidity is based only on the LaGuardia Airport weather station. Humidity is a new metric being considered by the NPCC3. Results show an increase between the 2020s and 2080s time slices of around 9% at each period's 10th percentile, while changes in the 90th percentile represent a 16% increase (Table 2.3). The uncertainty in these projections as characterized by the model ensemble 95% confidence bands (Fig. 2.4) is relatively large. Increases in specific humidity combined with increasing temperatures might lead to higher heat index (see Box 2.2), which has major consequences for human health and is a driver of peak energy demand for space cooling, as air conditioning systems remove sensible (temperature-related) and latent (moisture-related) heat from buildings. To assess combined air moisture and temperature impacts, concurrent hourly values must be used, rather than daily outputs from the model ensemble. Specific humidity: A measure of the amount of water in the atmosphere; the mass fraction of water vapor per unit mass of moist air. Absolute humidity: Mass of air per unit volume of moist air. Relative humidity: The ratio of water vapor pressure to the saturation vapor pressure. It measures how saturated with water vapor the atmosphere is. As air becomes more saturated with water vapor, it becomes more difficult for the human body to shed excess heat through evaporative cooling of perspiration. Heat index: A measure of the combined effects of temperature and relative humidity. It is defined by the National Weather Service. See Appendix 2.C of this chapter for an expanded discussion of how climate change is projected to impact the heat index. NPCC3 recommends further testing of this methodology for the development of new projections of record in NPCC4. Other definitions vary, including the use of standard deviations (Vavrus et al., 2006). Cold spell changes have been reported on regional scales (e.g., Europe, de Vries et al., 2012; China, Zhang et al., 2017; Northeast United States, Thibeault and Seth, 2014) and for global scales (Vavrus et al., 2006; Konrad, 1996) using GCM ensembles and long-term climate records. In most cases, cold days have shown decreases, and notably in Northern latitudes, it has been found that accelerated decreases of cold spells outpace increases in summer maxima (Thibeault and Seth, 2014). We used data from Central Park to establish a benchmark for cold spells. The 10th percentile threshold for cold days at this station was computed from the entire 1900–2017 record, with a value of 24.08 °F. In general, cold days per year decreased by 1.46 days every decade between 1900 and 2017, while days below freezing temperatures decreased at a rate of 1.85 days per decade (Fig. 2.5). This results in recent years having, on average, about 22 fewer days below freezing and 17 fewer cold days than in 1900. The rate of change of these trends is slightly lower than those reported for the entire Northeast by Thibeault and Seth (2014). For the case of New York City, the attribution of these rapid decreases of cold spells may be a combined effect of global warming and urbanization. Urbanization leads to the UHI effect, which tends to have a larger effect in the winter. The impact of global warming on climate implies an overall decrease in the number of cold extremes, while the number of warm extremes increases (Horton et al., 2015). However, recent persistent winter events of record cold weather in the Northeast United States and in other Northern Hemisphere regions raise concern of a possible connection to climate change. Both the science community (Screen et al., 2015) as well as the public (Lyons et al., 2018) have been engaged in research and discussion about cold air outbreaks associated with the Polar Vortex. An aspect of these discussions is the connection between the gradual disappearance of Arctic sea ice due to the polar amplification of global warming, the increase in atmospheric "blocking" events, and the slowing down and deepening of the wavy circulation in the midlatitudes (Screen and Simmonds, 2010; Overland et al., 2015). With the increase in amplitude and slowdown of atmospheric waves, cold air can flow down from the Arctic deep into the midlatitudes, and vice versa, warm air flows north. This creates protracted deviations from normal conditions in either place. In early January of 2014, a large cold air mass moved from Canada into the northern Great Plains states and made its way slowly to the Northeast. The unusual cold weather in the eastern half of the United States did not abate until April. At the same time, other areas in the Northern Hemisphere experienced record warm winter weather. Shorter events similar to this have happened since, as was the case during winter months in 2017–2018 and 2019. These events were connected to stratospheric warming, where the low-pressure vortex that is usually centered on the North Pole moves equatorward. This change in circulation is communicated down to the troposphere and results in anomalous weather situations during the winter season (Kretschmer et al., 2018; Screen et al., 2018). There has been much debate whether such events are linked to the gradual melting of sea ice in the Arctic, and it appears that the answer is that there is a link (Overland et al., 2015; Screen et al., 2018). This was shown in climate models (Zhang et al., 2018) and is consistent with the observation that polar vortex events are on the rise (Kretschmer et al., 2018). There is, however, no evidence that cold air outbreaks in the United States have increased as a result of this or other phenomena (Screen et al., 2015). The increase in polar vortex events was found to influence surface weather in Siberia, where a significant cooling of the average winter weather has been detected, in contrast with the observed warming elsewhere around the globe (Kretschmer et al., 2018; Zhang et al., 2018). New methodologies for projections of heat wave characteristics for the New York metropolitan region were tested in NPCC3 using bias-corrected climate model projections. For the early part of the century (2020s), these results are consistent with those of NPCC2. In the later part of the century (2050s and 2080s), the NPCC3 results display the potential for more intense heat events with longer durations. Results show large changes across all heat wave metrics throughout the 21st century. The high-emissions scenario (RCP8.5) projects, in many cases, several times larger effects than the medium-emissions scenario. The uncertainty of the projections increases through time. The new NPCC3 methods include humidity, which is projected to increase by more than 30% from baseline values. These increases in atmospheric humidity with extreme temperatures are likely to have large societal implications reflected in public health and energy demands. NPCC3 confirms the NPCC2 projections for heat waves, hot days, and cold days as those of record for New York City in planning for the impacts of climate change and recommends the incorporation of the new methodologies into revised projections of record in NPCC4. Future work in projecting extreme heat and humidity for the NPCC should be directed to incorporating the spatial distribution of these extreme heat events to account for coastal influence and UHI effects (e.g., sea breeze effects). This may require using regional climate models (RCMs) to dynamically downscale projections to finer spatial scales within the New York metropolitan region. Carrying this out for an ensemble of GCMs and RCMs will require large computational efforts. New methods may be needed to account for uncertainties in dynamic downscaling. See Appendix 2.C for an example of the possible approach, utilizing one GCM and one RCM for two time slices, as a potential guide for new research directions in NPCC4. NPCC2 projected quantitative changes in daily extreme rainfall amounts for 1 inch, 2 inches, and 4 inches (Table 2.4). NPCC2 also included a qualitative projection in relation to extreme rainfall, stating that heavy downpours in the New York metropolitan region are very likely to increase by the 2080s (Horton et al., 2015). NPCC3 does not provide new projections for heavy rainfall and confirms the NPCC2 projections as those of record for city planning and adaptation. It provides new analyses of the dynamics of heavy rainfall events in the New York metropolitan region recommended for use in developing new projections of record in NPCC4. NPCC3 focuses on observed annual rainfall (see Section 2.2) and observed heavy rainfall days in recent years compared to the NPCC2 2020s time slice projections. NPCC3 also analyzes the types of storm systems associated with heavy rainfall events, and the regional drivers of historical flash flooding events. This section also conducts a trend analysis of sub-daily heavy precipitation events at the 1-, 3-, 6-, and 24-h duration. Finally, this section explores ways to illustrate the spatial variation of urban flooding events. It is recommended that this work serve as a foundation for new projections of record for heavy rainfall that are to be developed in NPCC4. NPCC3 analyses of heavy downpours build on NPCC2 projections for daily extreme rainfall by more closely examining the past and present rainfall across New York City and across timescales. Additionally, NPCC3 includes observations of urban flooding (definition in Table 2.5), in New York City and surrounding areas. NPCC3 refocuses discussion from daily extreme rainfall to sub-daily "heavy downpours," defined as rarely occurring rainfall at less than daily timescales that can produce urban flooding. NPCC3 lays the groundwork for a new set of future projections in NPCC4 using these metrics. Extreme rainfall is defined as a rainfall amount that is a rare event, that is, one that approaches the end of the probability distribution of all events. In NPCC2, daily extreme rainfall in the current climate was represented by the number of occurrences of rainfall above 1 inch, 2 inches, or 4 inches per day at the Central Park weather station in New York City. Extreme rainfall measured at Central Park has significant year-to-year variation such that no statistically significant trends in extreme rainfall can be identified (Horton et al., 2015). (A statistically significant trend indicates that this trend in extreme rainfall would be unlikely to occur by chance). NPCC2 did note that the heaviest 1% of daily rainfalls have increased by approximately 70% between 1958 and 2011 in the Northeast (Horton et al., 2015). NPCC2 used the observed measurements as a baseline (Horton et al., 2015) for projections of extreme rainfall (Table 2.6; Horton et al., 2015). This section focuses on extreme rainfall by describing the approaches to heavy downpours in NPCC2 and NPCC3, studying regional drivers of daily and sub-daily heavy rainfall, providing a revised historical analysis of heavy rainfall across New York City, and summarizing new research projecting future changes in heavy downpours in the region. NPCC2 results included projections for extreme rainfall in the 2020

The health effects of climatic changes constitute an important research area, yet few researchers have reported city- or region-specific projections of temperature-related deaths based on assumptions about mitigation and adaptation. Herein, we provide quantitative projections for the number of additional deaths expected in the future, owing to the cold and heat in the city of Nanjing, China, based on 31 global circulation models (GCMs), two representative concentration pathways (RCPs) (RCP4.5 and RCP8.5), and three population scenarios [a constant scenario and two shared socioeconomic pathways (SSPs) (SSP2 and SSP5)], for the periods of 2010-2039, 2040-2069, and 2070-2099. The results show that for the period 2070-2099, the net number of temperature-related deaths can be comparable in the cases of RCP4.5/SSP2 and RCP8.5/SSP5 owing to the offsetting effects attributed to the increase of heat related deaths and the decrease of cold-related deaths. In consideration of this adaptation, we suggest that RCP4.5/SSP2 is a better future development pathway/scenario.

Mechanisms such as ice-shelf hydrofracturing and ice-cliff collapse may rapidly increase discharge from marine-based ice sheets. Here, we link a probabilistic framework for sea-level projections to a small ensemble of Antarctic ice-sheet (AIS) simulations incorporating these physical processes to explore their influence on global-mean sea-level (GMSL) and relative sea-level (RSL). We compare the new projections to past results using expert assessment and structured expert elicitation about AIS changes. Under high greenhouse gas emissions (Representative Concentration Pathway [RCP] 8.5), median projected 21st century GMSL rise increases from 79 to 146 cm. Without protective measures, revised median RSL projections would by 2100 submerge land currently home to 153 million people, an increase of 44 million. The use of a physical model, rather than simple parameterizations assuming constant acceleration of ice loss, increases forcing sensitivity: overlap between the central 90% of simulations for 2100 for RCP 8.5 (93-243 cm) and RCP 2.6 (26-98 cm) is minimal. By 2300, the gap between median GMSL estimates for RCP 8.5 and RCP 2.6 reaches >10 m, with median RSL projections for RCP 8.5 jeopardizing land now occupied by 950 million people (versus 167 million for RCP 2.6). The minimal correlation between the contribution of AIS to GMSL by 2050 and that in 2100 and beyond implies current sea-level observations cannot exclude future extreme outcomes. The sensitivity of post-2050 projections to deeply uncertain physics highlights the need for robust decision and adaptive management frameworks.