Plant phenology influences both the terrestrial carbon cycle and land-atmosphere interactions and, therefore, can potentially modify large-scale circulations in the atmosphere. However, considerable discrepancies are present among models and between model simulations and observations of plant phenology, adding large uncertainties to future climate projections. Here, we modified plant phenology in the Northern Hemisphere in the Community Earth System Model and conducted simulations to characterize how differences in plant phenology influence land-atmosphere coupling. Plant phenology changes the land surface and land-atmosphere interactions by directly modulating absorbed solar radiation and evapotranspiration and indirectly modifying cloud feedback and snow-albedo feedback. Over the Northern Hemisphere, the largest effects occur from March to June when seasonal deciduous phenology is modified from satellite-derived values to model simulations, which results in a >3 K increase in surface temperature that propagates to 500 hPa (similar to 5-km height). Phenology-induced changes in canopy evapotranspiration and surface temperature depend on soil moisture availability during the growing season. Surface temperature decreases significantly due to increasing latent heat flux and cloud reflection where soil moisture is abundant, while soil moisture control over evapotranspiration increases, and surface temperature remains little changed or even increases in more arid regions. Characterizing the influence of phenology on biogeophysical processes is critical, as significant impacts are present both at the land surface and in the atmospheric layers above. SIGNIFICANCE STATEMENT: Plant phenology influences both biogeochemical and biogeophysical processes in climate models. However, considerable discrepancies exist between satellite-derived phenology and state-of-the-art climate model simulations, and their influences on land-atmosphere coupling remain unclear. Here, we prescribe phenology discrepancies in different phenology plant function types (PFTs) to assess their influences on Earth's system and find significant PFT-dependent impacts. The largest impacts are in high-latitude regions dominated by seasonal deciduous PFTs in late winter to spring when increasing plant activity, cloud feedback, and snow-albedo feedback all cause increased surface temperature. Growing-season temperature decreases where soil moisture is abundant due to increasing evapotranspiration and cloud feedback. Understanding the potential influences of phenology discrepancies is critical for disentangling phenology-induced changes and uncertainties in future climate projections.

Phenological indices are an effective approach for assessing spatial and temporal patterns and variability in plant development. The Spring Indices (SI-x), two widely adopted phenological indices, have been used in recent decades to predict development of woody plants, and document changes in spring growth timing, especially in North America. However, these two indices (Leaf and Bloom) capture only two "moments" in the continuum of spring when quantities of thermal or photo/thermal energy, associated with seasonal events in plants, are accumulated, limiting their utility to characterize the remainder of the spring season. Further, the Spring Indices do not account for intraspecific variation, limiting their ability to reflect non-cloned plant development. To address these shortcomings, we developed a novel suite of phenological indices that encompass a broader span of the spring season. These indices were constructed using observations contributed to the USA National Phenology Network's Nature's Notebook platform across many non-cloned tree and shrub species' ranges, thereby incorporating differing regional responses within species due to genetic variations. Individual species model predictions of leaf or bloom timing exhibited an average mean absolute error of 8.55 days; most were improved by the inclusion of site-specific latitude, elevation, or 30-year average temperature. Leaf and bloom model outputs for individual species across the spring season were temporally aggregated into four leaf and bloom groups to produce a suite of Spring Development Indices (SDI). Accuracy of the SDI predictions was 0.89 days lower, on average, than the species models, but 2.65 days better than SI-x. Generally, all SDIs were highly correlated. The SDIs exhibiting the most difference from the others were Early leaf, Very Early bloom, and Late bloom. As such, these SDIs provide novel insights, beyond SI-x, into the relative timing of springseason "moments" across species in space and time.

Accurate simulation of plant phenology is important in Earth system models as phenology modulates land-atmosphere coupling and the carbon cycle. Evaluations based on grid-cell average leaf area index (LAI) can be misleading because multiple plant functional types (PFT) may be present in one model grid cell and PFTs with different phenology schemes have different LAI seasonal cycles. Here we examined PFT-specific LAI amplitudes and seasonal cycles in the Community Land Model versions 5.0 and 4.5 (CLM5.0 and CLM4.5) and their relationship with the onset of growing season triggers in the Northern Hemisphere. LAI seasonal cycle and spring onset in CLM show the best agreement with MODIS for temperature-dominated deciduous PFTs. Although the agreement in LAI amplitude between CLM5.0 and MODIS is better than CLM4.5, the agreement in seasonal cycles is worse in CLM5.0. CLM5.0 also simulates higher soil moisture and shows lower influences of soil moisture on LAI amplitudes and seasonal cycles. While productivity depends on the environmental factors to which the plant is exposed during any given growing season, differences in phenology sensitivity to its environment necessitate a decoupling between the seasonality of LAI and GPP, which in turn could lead to biases in the carbon cycle as well as surface energy balance and hence land-atmosphere interactions. Because the discrepancy not only depends on parameterizing phenology but phenology-environment relationship, future improvements to other model components (e.g., soil moisture) could better align the seasonal cycle of LAI and GPP.

Abstract Plant phenology regulates the carbon cycle and land‐atmosphere coupling. Currently, climate models often disagree with observations on the seasonal cycle of vegetation growth, partially due to how spring onset is measured and simulated. Here we use both thermal and leaf area index (LAI) based indicators to characterize spring onset in CMIP6 models. Although the historical timing varies considerably across models, most agree that spring has advanced in recent decades and will continue to arrive earlier with future warming. Across the Northern Hemisphere for the periods 1950–2014, 1981–2014, and 2015–2099 in the historical and SSP5‐8.5 simulations, thermal‐based indicators estimate spring advances of −0.7 ± 0.2, −1.4 ± 0.4, and −2.4 ± 0.7 days/decade, while LAI‐based indicators estimate −0.4 ± 0.3, −0.1 ± 0.3, and −1±1.1 days/decade. Thereby, LAI‐based indicators exhibit weaker trends toward earlier onset, leading to uncertainties from different indices being as large or larger than model uncertainty. Reconciling these discrepancies is critical for understanding future changes in spring onset.

Plant phenology modulates water and energy exchanges between the biosphere and the atmosphere and therefore influences planetary boundary layer (PBL) dynamics. Here we conduct a modeling experiment using the Community Earth System Model version 2, where plant phenology is prescribed based on satellite climatology in the control experiment. We then shift the timing of vegetation green-up and senescence in North America by one month earlier and later and investigate how shifting phenology could influence land-atmosphere interactions. Altering plant phenology modifies boundary layer fluxes through both direct influences on evapotranspiration and absorbed solar radiation and indirect effects through changes in low cloud fraction. The prescribed shift in phenology has significant but different influences on PBL dynamics and land-atmosphere coupling in the spring and fall in the Great Plains and Eastern United States. In the spring, earlier plant phenology significantly decreases PBL height in the Great Plains by more than 100 m. In the autumn, the Great Plains experience a significant increase in PBL height of over 100 m in the early fall while Eastern US exhibits a significant increase in the late fall when prescribed senescence is shifted earlier. As shifts in plant phenology alone can cause significant changes in PBL conditions at the seasonal timescale in the Great Plains and Eastern US, our experiments can help infer the potential location and magnitude of phenology-induced changes and provide useful information for observation-based analysis and model evaluation.

Exceptional drought events, known as megadroughts, have occurred on every continent outside Antarctica over the past ~2,000 years, causing major ecological and societal disturbances. In this Review, we discuss shared causes and features of Common Era (Year 1–present) and future megadroughts. Decadal variations in sea surface temperatures are the primary driver of megadroughts, with secondary contributions from radiative forcing and land–atmosphere interactions. Anthropogenic climate change has intensified ongoing megadroughts in south-western North America and across Chile and Argentina. Future megadroughts will be substantially warmer than past events, with this warming driving projected increases in megadrought risk and severity across many regions, including western North America, Central America, Europe and the Mediterranean, extratropical South America, and Australia. However, several knowledge gaps currently undermine confidence in understanding past and future megadroughts. These gaps include a paucity of high-resolution palaeoclimate information over Africa, tropical South America and other regions; incomplete representations of internal variability and land surface processes in climate models; and the undetermined capacity of water-resource management systems to mitigate megadrought impacts. Addressing these deficiencies will be crucial for increasing confidence in projections of future megadrought risk and for resiliency planning. Megadroughts can be defined as persistent, multi-year droughts that are exceptional compared with other regional events during the Common Era. This Review discusses palaeo reconstructions of megadroughts over the past 2,000 years, and outlines the impact of anthropogenic forcing on the severity and frequency of observed and projected events.

We investigated the predictability (forecast skill) of short-term droughts using the Palmer drought severity index (PDSI). We incorporated a sophisticated data training (of decadal range) to evaluate the improvement of forecast skill of short-term droughts (3 months). We investigated whether the data training of the synthetic North American Multi-Model Ensemble (NMME) climate has some influence on enhancing short-term drought predictability. The central elements are the merged information among PDSI and NMME with two postprocessing techniques. 1) The bias correction-spatial disaggregation (BC-SD) method improves spatial resolution by using a refined soil information introduced in the available water capacity of the PDSI calculation to assess water deficit that better estimates drought variability. 2) The ensemble model output statistic (EMOS) approach includes systematically trained decadal information of the multimodel ensemble simulations. Skill of drought forecasting improves when using EMOS, but BC-SD does not increase the forecast skill when compared with an analysis using BC (low spatial resolution). This study suggests that predictability forecast of drought (PDSI) can be extended without any change in the core dynamics of the model but instead by using the sophisticated EMOS postprocessing technique. We pointed out that using NMME without any postprocessing is of limited use in the suite of model variations of the NMME, at least for the U.S. Northeast. From our analysis, 1 month is the most extended range we should expect, which is below the range of the seasonal scale presented with EMOS (2 months). Thus, we propose a new design of drought forecasts that explicitly includes the multimodel ensemble signal.

The effects of volcanic eruptions on hurricane statistics are examined using two long simulations from the Community Earth System Model (CESM) Last Millennium Ensemble (LME). The first is an unforced control simulation, wherein all boundary conditions were held constant at their 850 CE values (LMEcontrol). The second is a “fully forced” simulation with time evolving radiative changes from volcanic, solar, and land use changes from 850 CE through present (LMEforced). Large tropical volcanic eruptions produce the greatest change in radiative forcing during this time period, which comprise the focus of this study. The Weather Research and Forecasting (WRF) model is used to dynamically downscale 150 control years of LMEcontrol and an additional 84 years of LMEforced for all mid-latitude volcanic eruptions between 1100 and 1850 CE. This time period was selected based on computational considerations. For each eruption, 2 years are dynamically downscaled. 23 of these volcanic eruptions are in the Northern Hemisphere and 19 are in the Southern Hemisphere. The effectiveness of the downscaling methodology is examined by applying the same downscaling approach to historical ERA-I reanalysis data and comparing the downscaled storm tracks and intensities to the International Best Track Archive for Climate Stewardship (IBTrACS) database. Hurricane statistics are then computed from both the downscaled control and downscaled forced LME simulations. Results suggest moderate effects on hurricanes from the average of all northern hemisphere eruptions, with the largest effects being from the volcanoes with the most aerosol forcing. More specifically, reductions in hurricane frequency, intensity, and lifetime following northern hemisphere eruptions are apparent. Strong evidence is also shown for correlation between eruption strength and changes in these diagnostics. The aggregate effect from both northern and southern hemisphere eruptions is minor. While reductions in frequency, intensity, and lifetime from northern hemisphere eruptions occur, the opposite effect is observed from southern hemisphere eruptions.

Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an “apples to apples” comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations.

Climate change might increase the frequency and severity of longer-lasting drought in the Caribbean, including in Hispaniola Island. Nevertheless, the hydroclimate changes projected by the state-of-the-art earth system models across the island remain unknown. Here, we assess 21st-century changes in hydroclimate over Hispaniola Island using precipitation, temperature, and surface soil moisture data from the 6th Phase of the Coupled Model Intercomparison Project (CMIP6). The resulting analysis indicates, as with the previous 5th Phase of CMIP (CMIP5) models, that Hispaniola Island might see a significant drying through the 21st century. The aridity appears to be robust in most of the island following the Shared Socioeconomic Pathways (SSP) 5–8.6, which assumes the “worst case” greenhouse gas emissions into the atmosphere. We find a significant reduction in both annual mean precipitation and surface soil moisture (soil’s upper 10 cm), although it appears to be more pronounced for precipitation (up to 26% and 11% for precipitation and surface soil moisture, respectively). Even though we provide insights into future hydroclimate changes on Hispaniola Island, CMIP6’s intrinsic uncertainties and native horizontal resolution precludes us to better assess these changes at local scales. As such, we consider future dynamical downscaling efforts that might help us to better inform policy-makers and stakeholders in terms of drought risk.

Projected changes in temperature and precipitation are expected to influence spring and autumn vegetation phenology and hence the length of the growing season in many ecosystems. However, the sensitivity of green‐up and senescence to climate remains uncertain. We analyzed 488 site years of canopy greenness measurements from deciduous forest broadleaf forests across North America. We found that the sensitivity of green‐up to temperature anomalies increases with increasing mean annual temperature, suggesting lower temperature sensitivity as we move to higher latitudes. Furthermore, autumn senescence is most sensitive to moisture deficits at dry sites, with decreasing sensitivity as mean annual precipitation increases. Future projections suggest North American deciduous forests will experience higher sensitivity to temperature in the next 50 years, with larger changes expected in northern regions than in southern regions. Our study highlights how interactions between long‐term and short‐term changes in the climate system influence green‐up and senescence.

Droughts of the future are likely to be more frequent, severe, and longer lasting than they have been in recent decades, but drought risks will be lower if greenhouse gas emissions are cut aggressively. This review presents a synopsis of the tools required for understanding the statistics, physics, and dynamics of drought and its causes in a historical context. Although these tools have been applied most extensively in the United States, Europe, and the Amazon region, they have not been as widely used in other drought-prone regions throughout the rest of the world, presenting opportunities for future research. Water resource managers, early career scientists, and veteran drought researchers will likely see opportunities to improve our understanding of drought.

Interannual variability in the global carbon cycle is largely due to variations in carbon uptake by terrestrial ecosystems, yet linkages between climate variability and variability in the terrestrial carbon cycle are not well understood at the global scale. Using a 30-year satellite record of semi-monthly leaf area index (LAI), we show that four modes of climate variability − El Niño/Southern Oscillation, the North Atlantic Oscillation, the Atlantic Meridional Mode, and the Indian Ocean Dipole Mode − strongly impact interannual vegetation growth patterns, with 68% of the land surface impacted by at least one of these teleconnection patterns, yet the spatial distribution of these impacts is heterogeneous. Considering the patterns' impacts by biome, none has an exclusively positive or negative relationship with LAI. Our findings imply that future changes in the frequency and/or magnitude of teleconnection patterns will lead to diverse changes to the terrestrial biosphere and the global carbon cycle.

Multidecadal hydroclimate variability has been expressed as "megadroughts" (dry periods more severe and prolonged than observed over the twentieth century) and corresponding "megapluvial" wet periods in many regions around the world. The risk of such events is strongly affected by modes of coupled atmosphere-ocean variability and by external impacts on climate. Accurately assessing the mechanisms for these interactions is difficult, since it requires large ensembles of millennial simulations as well as long proxy time series. Here, the Community Earth System Model (CESM) Last Millennium Ensemble is used to examine statistical associations among megaevents, coupled climate modes, and forcing from major volcanic eruptions. El Nino-Southern Oscillation (ENSO) strongly affects hydroclimate extremes: larger ENSO amplitude reduces megadrought risk and persistence in the southwestern United States, the Sahel, monsoon Asia, and Australia, with corresponding increases in Mexico and the Amazon. The Atlantic multidecadal oscillation (AMO) also alters megadrought risk, primarily in the Caribbean and the Amazon. Volcanic influences are felt primarily through enhancing AMO amplitude, as well as alterations in the structure of both ENSO and AMO teleconnections, which lead to differing manifestations of megadrought. These results indicate that characterizing hydroclimate variability requires an improved understanding of both volcanic climate impacts and variations in ENSO/AMO teleconnections.

Accurate assessments of future climate impacts require realistic simulation of interannual-century-scale temperature and precipitation variability. Here, well-constrained paleoclimate data and the latest generation of Earth system model data are used to evaluate the magnitude and spatial consistency of climate variance distributions across interannual to centennial frequencies. It is found that temperature variance generally increases with time scale in patterns that are spatially consistent among models, especially over the mid-and high-latitude oceans. However, precipitation is similar to white noise across much of the globe. When Earth system model variance is compared to variance generated by simple autocorrelation, it is found that tropical temperature variability in Earth system models is difficult to distinguish from variability generated by simple autocorrelation. By contrast, both forced and unforced Earth system models produce variability distinct from a simple autoregressive process over most high-latitude oceans. This new analysis of tropical paleoclimate records suggests that low-frequency variance dominates the temperature spectrum across the tropical Pacific and Indian Oceans, but in many Earth system models, interannual variance dominates the simulated central and eastern tropical Pacific temperature spectrum, regardless of forcing. Tropical Pacific model spectra are compared to spectra from the instrumental record, but the short instrumental record likely cannot provide accurate multidecadal-centennial-scale variance estimates. In the coming decades, both forced and natural patterns of decade-century-scale variability will determine climate-related risks. Underestimating low-frequency temperature and precipitation variability may significantly alter our understanding of the projections of these climate impacts.