Abstract. Water availability for societies and ecosystems depends upon Terrestrial Water Storage (TWS), yet global, spatially resolved measurements are largely unavailable before the advent of the Gravity Recovery and Climate Experiment (GRACE) gravimetric measurements in 2002. By exploiting a larger set of model and observations-based datasets than previously considered, along with statistical and machine learning techniques, we advance understanding of TWS changes since the 1980s, including accounting for human water management (HWM). A decline in TWS during 2002–2019 is identified for three global hydrologic models with HWM and bias-corrected precipitation forcing (-0.91 to -0.06 mm yr-1) with only one showing larger decreases than observed by GRACE observations (-0.80 mm y-1). We further identify a longer-term decline in TWS during 1980–2019 in these models, linked with regional precipitation decreases and the net effects of HWM through TWS drawdowns over northern India, southwest U.S. and northeastern China, yet the amplitude of the global land trends remains poorly quantified, ranging from -0.72 to +0.04 mm y-1. Statistical / Machine Learning (ML) reconstructions are found to match GRACE variability but their fidelity in the pre-GRACE/FO period remains unknown. A stronger decline in TWS since 1980 in the European Centre for Medium-range Weather Forecasts 5th generation reanalysis (ERA5) enhanced land component (ERA5-Land) is linked to an artificial drop in precipitation around 2000–2002 in ERA5 that is most pronounced over equatorial central Africa, northeastern China and the northern Argentina / La Plata region. Our findings urge caution in inferring changes in hydroclimate variables from ERA5-Land and other reanalyses due to inhomogeneities in the assimilated observational data. Continued emphasis on bias corrections to hydrometeorological data and better modeling of HWM are crucial to improving all retrospective analyses of changes in land surface hydrology and terrestrial water stores.

The 1993 U.S. Midwest summer flood occurred in a year marked by a number of apparently disparate climate extremes including an unusually cold spring Pacific warm pool, a record deep spring Aleutian low, and record wet conditions that spanned the Northern Hemisphere midlatitude land areas during June and July. Here, we provide a dynamical framework that links these extremes and accounts for the uniqueness of the Midwest flooding event. In particular, we show that the deep springtime Aleutian low was part of a wave response forced by unusually strong precipitation/heating anomalies in the equatorial Pacific just west of the date line—heating that was linked to the unusually cold Pacific warm pool juxtaposed to the east with positive SST anomalies tied to a weak but unusually timed El Niño event. The deep springtime Aleutian low in turn produced unusually cold summer North Pacific SSTs and set the stage for the summer’s eddy-driven enhancement of the midlatitude jet and unusually strong hemisphericwide transient (baroclinic) wave activity. The resulting transient vorticity forcing produced two pronounced stationary waves—one in June anchored over northern Eurasia, and another in July anchored over the Pacific/North American region, resulting in record precipitation anomalies over northwestern Eurasia and the northern Great Plains, respectively.

Recent increase in extreme wildfire events has led to major health and environmental consequences across the globe. These adverse impacts underlined the need for better understanding of this phenomenon and to formulate mitigating actions. While previous research has focused on local weather drivers of wildfires, our knowledge about their large-scale climatic controls remains limited, especially in tropical Africa, which stands out as a global hotspot for fire emissions. Here, we show that interannual variability of carbon emission due to fires in the southern Congo Basin is strongly linked to low-level winds that are controlled by the Indian Ocean subtropical high. The interhemispheric transport of these emissions to West Africa relies on the intensity and position of both Indian and South Atlantic subtropical highs. Combined effects of this transport mechanism and carbon production in the source region explain a majority of the interannual variability of black carbon in West Africa. Emissions from biomass burning in different parts of the world impact air quality and climatic conditions both locally and in areas distant from the origin. Tropical Africa stands out as a hotspot of fire emissions that are primarily human-induced. In this study, we show that the year-to-year variability of the carbon emission caused by these fires in the southern Congo Basin is directly related to the local winds that are in turn controlled by conditions in the Indian Ocean. Specifically, an intensified subtropical high in the Indian Ocean leads to stronger local winds, exacerbating fire emissions in the Congo Basin. These emissions then traverse the Atlantic Ocean, affecting West Africa-an interhemispheric journey made by the atmospheric circulation patterns. Fire emissions in the Congo Basin increase when local winds are strengthened due to intensification of the Indian Ocean subtropical high Interannual variability of black carbon in West Africa is driven by fire emissions in the Congo Basin and winds over the adjacent oceans

Accurate diagnosis of regional atmospheric and surface energy budgets is critical for understanding the spatial distribution of heat uptake associated with the Earth’s energy imbalance (EEI). This contribution discusses frameworks and methods for consistent evaluation of key quantities of those budgets using observationally constrained data sets. It thereby touches upon assumptions made in data products which have implications for these evaluations. We evaluate 2001–2020 average regional total (TE) and dry static energy (DSE) budgets using satellite-based and reanalysis data. For the first time, a consistent framework is applied to the ensemble of the 5th generation European Reanalysis (ERA5), version 2 of modern-era retrospective analysis for research and applications (MERRA-2), and the Japanese 55-year Reanalysis (JRA55). Uncertainties of the computed budgets are assessed through inter-product spread and evaluation of physical constraints. Furthermore, we use the TE budget to infer fields of net surface energy flux. Results indicate biases < 1 W/m2 on the global, < 5 W/m2 on the continental, and 15 W/m2 on the regional scale. Inferred net surface energy fluxes exhibit reduced large-scale biases compared to surface flux data based on remote sensing and models. We use the DSE budget to infer atmospheric diabatic heating from condensational processes. Comparison to observation-based precipitation data indicates larger uncertainties (10–15 Wm−2 globally) in the DSE budget compared to the TE budget, which is reflected by increased spread in reanalysis-based fields. Continued validation efforts of atmospheric energy budgets are needed to document progress in new and upcoming observational products, and to understand their limitations when performing EEI research.

As observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions, global terrestrial water storage (TWS), excluding ice sheets and glaciers, declined rapidly between May 2014 and March 2016. By 2023, it had not yet recovered, with the upper end of its range remaining 1 cm equivalent height of water below the upper end of the earlier range. Beginning with a record-setting drought in northeastern South America, a series of droughts on five continents helped to prevent global TWS from rebounding. While back-to-back El Niño events are largely responsible for the South American drought and others in the 2014–2016 timeframe, the possibility exists that global warming has contributed to a net drying of the land since then, through enhanced evapotranspiration and increasing frequency and intensity of drought. Corollary to the decline in global TWS since 2015 has been a rise in barystatic sea level (i.e., global mean ocean mass). However, we find no evidence that it is anything other than a coincidence that, also in 2015, two estimates of barystatic sea level change, one from GRACE/FO and the other from a combination of satellite altimetry and Argo float ocean temperature measurements, began to diverge. Herein, we discuss both the mechanisms that account for the abrupt decline in terrestrial water storage and the possible explanations for the divergence of the barystatic sea level change estimates.

In late December of 2022 and the fi rst half of January 2023, an unprecedented series of atmospheric rivers (ARs) produced near -record heavy rains and fl ooding over much of California. Here, we employ the NASA GEOS AGCM run in a "replay" mode, together with more idealized simulations with a stationary wave model, to identify the remote forcing regions, mechanisms, and underlying predictability of this fl ooding event. In particular, the study addresses the underlying causes of a persistent positive Paci fi c-North American (PNA)-like circulation pattern that facilitated the development of the ARs. We show that the pattern developed in late December as a result of vorticity forcing in the North Paci fi c jet exit region. We further provide evidence that this vorticity forcing was the result of a chain of events initiated in mid -December with the development of a Rossby wave (as a result of forcing linked to the MJO) that propagated from the northern Indian Ocean into the North Paci fi c. As such, both the initiation of the event and the eventual development of the PNA depended critically on internally generated Rossby wave forcings, with the North Paci fi c jet playing a key role. This, combined with contemporaneous SST (La Ni & ntilde;a) forcing that produced a circulation response in the AGCM that was essentially opposite to the positive PNA, underscores the fundamental lack of predictability of the event at seasonal time scales. Forecasts produced with the GEOS-coupled model suggest that useful skill in predicting the PNA and extreme precipitation over California was in fact limited to lead times shorter than about 3 weeks.

In this study, we investigate the representation of the global mean energy balance components in 10 atmospheric reanalyses, and compare their magnitudes with recent reference estimates as well as the ones simulated by the latest generation of climate models from the 6th phase of the coupled model intercomparison project (CMIP6). Despite the assimilation of comprehensive observational data in reanalyses, the spread amongst the magnitudes of their global energy balance components generally remains substantial, up to more than 20 Wm−2 in some quantities, and their consistency is typically not higher than amongst the much less observationally constrained CMIP6 models. Relative spreads are particularly large in the reanalysis global mean latent heat fluxes (exceeding 20

The Clouds and the Earth's Radiant Energy System (CERES) project has now produced over two decades of observed data on the Earth's Energy Imbalance (EEI) and has revealed substantive trends in both the reflected shortwave and outgoing longwave top-of-atmosphere radiation components. Available climate model simulations suggest that these trends are incompatible with purely internal variability, but that the full magnitude and breakdown of the trends are outside of the model ranges. Unfortunately, the Coupled Model Intercomparison Project (Phase 6) (CMIP6) protocol only uses observed forcings to 2014 (and Shared Socioeconomic Pathways (SSP) projections thereafter), and furthermore, many of the ‘observed' drivers have been updated substantially since the CMIP6 inputs were defined. Most notably, the sea surface temperature (SST) estimates have been revised and now show up to 50% greater trends since 1979, particularly in the southern hemisphere. Additionally, estimates of short-lived aerosol and gas-phase emissions have been substantially updated. These revisions will likely have material impacts on the model-simulated EEI. We therefore propose a new, relatively low-cost, model intercomparison, CERESMIP, that would target the CERES period (2000-present), with updated forcings to at least the end of 2021. The focus will be on atmosphere-only simulations, using updated SST, forcings and emissions from 1990 to 2021. The key metrics of interest will be the EEI and atmospheric feedbacks, and so the analysis will benefit from output from satellite cloud observation simulators. The Tier 1 request would consist only of an ensemble of AMIP-style simulations, while the Tier 2 request would encompass uncertainties in the applied forcing, atmospheric composition, single and all-but-one forcing responses. We present some preliminary results and invite participation from a wide group of models.

Despite the dire conclusions of the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports in terms of global warming and its impacts on Earth’s climate, ecosystems and human society, a skepticism claiming that the projected global warming is alarmist or, at least, overestimated, still persists. Given the years passed since the future climate projections that served as basis for the IPCC 4th, 5th and the upcoming 6th Assessment Reports were released, it is now possible to answer this fundamental question if the projected global warming has been over or underestimated.This work presents a comparison between CMIP3, CMIP5 and CMIP6 future surface temperature projections and observations, by comparing 21 years of CMIP3, 15 years of CMIP5 and 6 years of CMIP6 future temperature projections with observations. It should be highlighted that only the future temperature projections from the CMIP scenarios are under evaluation in this study, not the scenarios per se.The results show that global temperature increase over land areas projected by all CMIPs and future climate scenarios here analyzed are well in line with the observations, being noticeable that the observed warming is closer to the upper level of the projected ones. Moreover, CMIPs project a global warming slightly lower than the one shown by observations, and CMIPs climate projections that consider future climate scenarios with higher GHG concentrations appear to be the more realistic ones.These results show that CMIPs future warming projections have been slightly conservative up to 2020, which can suggest a similar cold bias in their warming projections up to the end of the current century. However, given the short future periods here analyzed, inferences about warming at longer timescales cannot be done with confidence, since the models internal variability can play a relevant role on timescales of 20 years and less.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0/). Corresponding author: Arianna Valmassoi, avalmass@uni-bonn.de

Abstract Extreme climate events are societally harmful and have increased in frequency and intensity in recent decades. Indices based on temperature and precipitation are a valuable way to quantify climate extremes. Certain indices are defined relative to percentiles, which are dependent on a climatological baseline period. In this study, indices computed using temperature and precipitation from the Modern Era Retrospective Analysis for Research and Applications, Version 2 are calculated using percentiles from three baseline periods: 1981–2010, 1991–2020 and 1981–2020. Updating the baseline period from 1981 to 2010 to 1991–2020 leads to significant changes in the quantification of temperature and precipitation extremes over the United States over 1980–2021. Using the later baseline period indicates more cold extremes, fewer warm extremes, and fewer but more intense precipitation extremes throughout the US, with regional variation. Changing the baseline period can mislead the public and decision makers, potentially undermining the appropriate response to climate‐related health risks.

The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth’s energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described.

Atmospheric Infrared Sounder (AIRS) aboard the National Aeronautics and Space Administration (NASA) Aqua satellite has been operating since September 2002. Its information content, superb instrument performance, and dense sampling pattern make the AIRS radiances an invaluable data set for climate studies. The trends of global‐mean, nadir‐view, clear‐sky AIRS radiances from 2003 to 2020 are studied here, together with the counterparts of synthetic radiances based on two reanalyzes, European Centre for Medium‐Range Weather Forecasts Reanalysis V5 (ECMWF ERA5) and NASA Goddard Earth Observing System V5.4.1 (GEOS‐5.4.1; a reanalysis product without assimilation of hyperspectral radiances such as AIRS). The AIRS observation shows statistically significant negative trends in most of its CO2 channels, positive but non‐significant trends in the channels over the window regions, and statistically significant positive trends in some of its H2O channels. The best agreements between observed and simulated radiance trends are seen over the CO2 tropospheric channels, while the observed and simulated trends over the CO2 stratospheric channels are opposite. ERA5 results largely agree with the AIRS observation over the H2O channels. The comparison in the H2O channels helps reveal a data continuity issue in the GEOS‐5.4.1. Contributions from individual variables to the radiance trends are also assessed by performing separate simulations. This study provides the first synopsis of the global‐mean trend of AIRS radiances over all its thermal‐IR channels.

Caption: Lightning discharges appear in various colours depending on the scatter of light inside the thundercloud and in the atmosphere.The intracloud lightning discharges in the centre of the thundercloud appear to be white with a bluish tint, and the cloud-to-ground discharge below appears to be orange.The right hand side of the thundercloud exhibits a green tint that is attributed to the unique composition of hydrometeors inside the thundercloud.The photo was taken in the late evening of 10 September 2013, near Tarragona in northeastern Spain.

For similar to 100 years, the continental patterns of avian migration in North America have been described in the context of three or four primary flyways. This spatial compartmentalization often fails to adequately reflect a critical characterization of migration-phenology. This shortcoming has been partly due to the lack of reliable continental-scale data, a gap filled by our current study. Here, we leveraged unique radar-based data quantifying migration phenology and used an objective regionalization approach to introduce a new spatial framework that reflects interannual variability. Therefore, the resulting spatial classification is intrinsically different from the "flyway concept." We identified two regions with distinct interannual variability of spring migration across the contiguous United States. This data-driven framework enabled us to explore the climatic cues affecting the interannual variability of migration phenology, "specific to each region" across North America. For example, our "two-region" approach allowed us to identify an east-west dipole pattern in migratory behavior linked to atmospheric Rossby waves. Also, we revealed that migration movements over the western United States were inversely related to interannual and low-frequency variability of regional temperature. A similar link, but weaker and only for interannual variability, was evident for the eastern region. However, this region was more strongly tied to climate teleconnections, particularly to the east Pacific-North Pacific (EP-NP) pattern. The results suggest that oceanic forcing in the tropical Pacific-through a chain of processes including Rossby wave trains-controls the climatic conditions, associated with bird migration over the eastern United States. Our spatial platform would facilitate better understanding of the mechanisms responsible for broadscale migration phenology and its potential future changes.

Record-breaking heatwaves and wildfires immersed Siberia during the boreal spring of 2020 following an anomalously warm winter. Springtime heatwaves are becoming more common in the region, with statistically significant trends in the frequency, magnitude, and duration of heatwave events over the past four decades. Mechanisms by which the heatwaves occur and contributing factors differ by season. Winter heatwave frequency is correlated with the atmospheric circulation, particularly the Arctic Oscillation, while the frequency of heatwaves during the spring months is highly correlated with aspects of the land surface including snow cover, albedo, and latent heat flux. Idealized AMIP-style experiments are used to quantify the contribution of suppressed Arctic sea ice and snow cover over Siberia on the atmospheric circulation, surface energy budget, and surface air temperature in Siberia during the winter and spring of 2020. Sea ice concentration contributed to the strength of the stratospheric polar vortex and Arctic Oscillation during the winter months, thereby influencing the tropospheric circulation and surface air temperature over Siberia. Warm temperatures across the region resulted in an earlier-than-usual recession of the winter snowpack. The exposed land surface contributed to up to 20% of the temperature anomaly during the spring through the albedo feedback and changes in the ratio of the latent and sensible heat fluxes. This, in combination with favorable atmospheric circulation patterns, resulted in record-breaking heatwaves in Siberia in the spring of 2020.

Cloud macrophysical changes over the Pacific from 2007 to 2017 are examined by combining CALIOP and CloudSat (CALCS) active-sensor measurements, and these are compared with MODIS passive-sensor observations. Both CALCS and MODIS capture well-known features of cloud changes over the Pacific associated with meteorological conditions during El Niño-Southern Oscillation (ENSO) events. For example, mid (cloud tops at 3–10 km) and high (cloud tops at 10–18 km) cloud amounts increase with relative humidity (RH) anomalies. However, a better correlation is obtained between CALCS cloud volume and RH anomalies, confirming more accurate CALCS cloud boundaries than MODIS. Both CALCS and MODIS show that low cloud (cloud tops at 0–3 km) amounts increase with EIS and decrease with SST over the eastern Pacific, consistent with earlier studies. It is also further shown that the low cloud amounts do not increase with positive EIS anomalies if SST anomalies are positive. While similar features are found between CALCS and MODIS low cloud anomalies, differences also exist. First, compared to CALCS, MODIS shows stronger anti-correlation between low and mid/high cloud anomalies over the central and western Pacific, which is largely due to the limitation in detecting overlapping clouds from passive MODIS measurements. Second, compared to CALCS, MODIS shows smaller impacts of mid and high clouds on the low troposphere (< 3 km). The differences are due to the underestimation of MODIS cloud layer thicknesses of mid and high clouds.