General Circulation Model Research Articles

Dominant Sources of Uncertainty for Downscaled Climate: A Military Installation Perspective

AbstractWhile the Department of Defense (DoD) infrastructure is no stranger to extremes, recent events have been unprecedented, with climate change acting as a growing risk multiplier. To assess the level of exposure of DoD installations to extreme weather and climate events, site‐specific climate information is needed. One way to bridge the scale gap between outputs from existing global climate models (GCMs) and sites is climate downscaling. This makes the information more relevant for impact assessment at the DoD installation and facility scale. However, downscaling GCMs is beset by a myriad of challenges and sources of uncertainty, and downscaling methods were not designed with specific infrastructure planning and design needs in mind. Here, we evaluate state‐of‐the‐science dynamical downscaling and statistical downscaling and bias correction for climate variables (i.e., temperature and precipitation) at the daily scale. We also combine downscaling approaches in novel ways to optimize computational efficiency and reduce uncertainty. Furthermore, we examine the sensitivity of the downscaled outputs to the choice of reference data and quantify the relative uncertainty related to downscaling approach, reference data, and other factors across the climate variables and aggregation scales. Results show that empirical quantile mapping (EQM), a statistical downscaling, consistently performs well and has less sensitivity to the choice of reference data. Moreover, the hybrid downscaling that leverages EQM improves the performance of dynamical downscaling. Our findings highlight that the choice of reference data dominates uncertainties in temperature downscaling, while their role is more muted for precipitation but still non‐negligible.

Incorporation of RCM-simulated spatial details into climate change projections derived from global climate models

Regional climate models (RCMs) exhibit greater potential than global models (GCMs) in capturing geographical details of climate change arising from orography and land–water distribution, but dynamical downscalings are only available for a limited number of GCMs. The full GCM ensembles are much more representative. Furthermore, the current EURO-CORDEX RCM runs most likely underestimate future warming. Thus, neither GCMs nor RCMs as such constitute an ideal tool for preparing reliable spatially detailed climate projections. This study introduces an easy-to-use GCM-RCM hybrid method that takes advantage of the best properties of both model categories. The large-scale response is adopted from GCM simulations, but the pattern is enriched with RCM-simulated details. For temperature projections, the procedure resembles the conventional pattern-scaling technique. However, the spatial averages of temperature change used for scaling are calculated over an area surrounding each grid point, either by giving an equal weight to the entire area or by taking into account the land–sea distribution. For precipitation, a linearised version of the method has been formulated. The method is demonstrated by integrating spatial details from 12 EURO-CORDEX RCM simulations with the CMIP6 multi-GCM mean projection. The resulting temperature responses include RCM-generated spatial details of up to ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 1 ∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ$$\\end{document}C while effectively correcting the general tendency of RCMs to underestimate warming in Europe. For precipitation, geographical details originating from the different CORDEX runs tend to diverge, resulting in a low signal-to-noise ratio. This probably reflects the substantial impact of internal variability on small-scale changes in precipitation.

Radiative effects of precipitation on the global energy budget and Arctic amplification

Radiative forcing is an essential metric for accurate climate prediction. Clouds are a well-known source of uncertainty, but the radiative effects of precipitation (REP) are poorly understood and excluded from most general circulation models (GCMs). This is because conventional GCMs treat precipitation diagnostically, and thus, are transparent to shortwave and longwave radiation. In this study, we investigated the REP at global and regional scales by employing three sub-models incorporating (1) diagnostic precipitation, (2) prognostic precipitation without REP, and (3) prognostic precipitation with REP. We found that REP alters not only the local thermodynamic profile but also the remote precipitation rate and distribution through changes in atmospheric circulation. The polar surface temperature increases by more than 1 K in the winter when considering REP. The 34 CMIP6 models show systematic differences in Arctic amplification depending on REP, emphasising that GCMs should include REP to improve confidence in simulating atmosphere-ocean-cryosphere interactions.

Past and future of the Arctic sea ice in High-Resolution Model Intercomparison Project (HighResMIP) climate models

Abstract. We examine the past and projected changes in Arctic sea ice properties in six climate models participating in the High-Resolution Model Intercomparison Project (HighResMIP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Within HighResMIP, each of the experiments is run using a reference resolution configuration (consistent with typical CMIP6 runs) and using higher-resolution configurations. The role of horizontal grid resolution in both the atmosphere model component and the ocean model component in reproducing past and future changes in the Arctic sea ice cover is analysed. Model outputs from the coupled historical (hist-1950) and future (highres-future) runs are used to describe the multi-model, multi-resolution representation of the Arctic sea ice and to evaluate the systematic differences (if any) that resolution enhancement causes. Our results indicate that there is not a strong relationship between the representation of sea ice cover and the ocean/atmosphere grids; the impact of horizontal resolution depends rather on the sea ice characteristic examined and the model used. However, the refinement of the ocean grid has a more prominent effect compared to the refinement of the atmospheric one, with eddy-permitting ocean configurations generally providing more realistic representations of sea ice area and sea ice edges. All models project substantial sea ice shrinking: the Arctic loses nearly 95 % of sea ice volume from 1950 to 2050. The model selection based on historical performance potentially improves the accuracy of the model projections and predicts that the Arctic will turn ice-free as early as 2047. Along with the overall sea ice loss, changes in the spatial structure of the total sea ice and its partition in ice classes are noticed: the marginal ice zone (MIZ) will dominate the ice cover by 2050, suggesting a shift to a new sea ice regime much closer to the current Antarctic sea ice conditions. The MIZ-dominated Arctic might drive development and modification of model physics and parameterizations in the new generation of general circulation models (GCMs).

Climate change impact assessment on the hydrological response of the Tawa basin for sustainable water management

Sustainable water resources planning and management are essential for meeting the needs of both current and future generations while mitigating environmental impacts. River basins, integral components of ecosystems, are pivotal in implementing well-balanced and responsible management strategies. River basins serve as crucial sources for freshwater supply, irrigation, hydropower generation, and industrial activities. In the face of climate change, it becomes imperative to prioritize the hydrological impact assessment of river basins. This prioritization aims to ensure the resilience of water systems, recognizing the heightened challenges posed by changing climatic conditions. The present study examines the projected changes in the mean and high flow of Tawa River, an important Narmada River tributary that is critical for agriculture and hydropower generation using two conceptual lumped hydrological models, MIKE 11 NAM and GRJ4 respectively, and thirteen CMIP6 global climate models (GCMs) for two shared socioeconomic pathways (SSPs), SSP2-45 and SSP5-85, in the near (2030–2060) and far (2070–2100) futures. Our findings reveal a significant rise in both mean monthly and extreme discharge levels for two scenarios: SSP2-4.5 and SSP5-8.5. Notably, the projections indicate that the mean annual discharge, extreme discharge levels, and their frequency are anticipated to rise considerably more dramatically in the period of far future (2070–2100), particularly under the SSP5-8.5 scenario. This study underlines the importance of establishing a mitigation strategy for sustainable water resources and increased community resilience to climate change in the Tawa River Basin.

The Metallicity and Carbon-to-oxygen Ratio of the Ultrahot Jupiter WASP-76b from Gemini-S/IGRINS

Measurements of the carbon-to-oxygen (C/O) ratios of exoplanet atmospheres can reveal details about their formation and evolution. Recently, high-resolution cross-correlation analysis has emerged as a method of precisely constraining the C/O ratios of hot Jupiter atmospheres. We present two transits of the ultrahot Jupiter WASP-76b observed between 1.4 and 2.4 μm with the high-resolution Immersion GRating INfrared Spectrometer on the Gemini-S telescope. We detected the presence of H2O, CO, and OH at signal-to-noise ratios of 6.93, 6.47, and 3.90, respectively. We performed two retrievals on this data set. A free retrieval for abundances of these three species retrieved a volatile metallicity of C+OH=−0.70−0.93+1.27 , consistent with the stellar value, and a supersolar carbon-to-oxygen ratio of C/O =0.80−0.11+0.07 . We also ran a chemically self-consistent grid retrieval, which agreed with the free retrieval within 1σ but favored a slightly more substellar metallicity and solar C/O ratio ( C+OH=−0.74−0.17+0.23 and C/O =0.59−0.14+0.13 ). A variety of formation pathways may explain the composition of WASP-76b. Additionally, we found systemic (V sys) and Keplerian (K p ) velocity offsets which were broadly consistent with expectations from 3D general circulation models of WASP-76b, with the exception of a redshifted V sys for H2O. Future observations to measure the phase-dependent velocity offsets and limb differences at high resolution on WASP-76b will be necessary to understand the H2O velocity shift. Finally, we find that the population of exoplanets with precisely constrained C/O ratios generally trends toward super-solar C/O ratios. More results from high-resolution observations or JWST will serve to further elucidate any population-level trends.

Changes in the tropical upper-tropospheric zonal momentum balance due to global warming

Abstract. We study the zonal momentum budget of the deep upper tropics in the context of present and future climates. In the zonal mean, as is known, a robust balance exists between the acceleration by the horizontal eddy momentum flux convergence and the deceleration by the mean meridional momentum advection. During summer, climatological stationary Rossby waves over the Asian monsoon longitudes converge westerly momentum into the tropics and are the primary contributors to the eddy term. During winter, anomalous westerly winds over the tropical east Pacific allow extratropical waves to propagate into the deep tropics, where they tend to break and decelerate the flow. When integrated over all longitudes, we find that eddies from these two regions sum constructively in summer and destructively in winter, always yielding a net positive momentum forcing that balances the mean flow term. The state-of-the-art CMIP6 suite qualitatively captures these features in the historical run and shows that the momentum fluxes change due to global warming. In summer, stationary eddy circulations in the Asian monsoon zone weaken in the upper troposphere (UT) but strengthen in the lower stratosphere (LS). Greater upward mass flux from the troposphere forces a stronger divergence and a more intense circulation in the LS following a Sverdrup vorticity balance. We observe this strengthening of summertime tropical and subtropical stationary waves in the LS over all longitudes and verify it in an idealized aquaplanet general circulation model experiment. In winter, we find that eddy westerlies over the east Pacific longitudes decrease in strength due to the expected weakening of the subtropical stationary waves with warming. This causes a significant decrease in the propagation of extratropical waves into this region, along with a drop in eddy potential vorticity fluxes associated with these waves. Thus, apart from the mean meridional flux, which weakens due to the projected weakening of the Hadley cells, our analysis of warming simulations clearly suggests significant and robust changes in the eddy momentum fluxes in the deep tropics. Potential implications of these changes in the context of the zonal mean flow and regional circulations are discussed.

Upstream surface roughness and terrain are strong drivers of contrast in tornado potential between North and South America

Central North America is the global hotspot for tornadoes, fueled by elevated terrain of the Rockies to the west and a source of warm, moist air from equatorward oceans. This conventional wisdom argues that central South America, with the Andes to the west and Amazon basin to the north, should have a "tornado alley" at least as active as central North America. Central South America has frequent severe thunderstorms yet relatively few tornadoes. Here, we show that conventional wisdom is missing an important ingredient specific to tornadoes: a smooth, flat ocean-like upstream surface. Using global climate model experiments, we show that central South American tornado potential substantially increases if its equatorward land surface is smoothed and flattened to be ocean-like. Similarly, we show that central North American tornado potential substantially decreases if its equatorward ocean surface is roughened to values comparable to forested land. A rough upstream surface suppresses the formation of tornadic environments principally by weakening the poleward low-level winds, characterized by a weakened low-level jet east of the mountain range. Results are shown to be robust for any midlatitude landmass using idealized experiments with a simplified continent and mountain range. Our findings indicate that large-scale upstream surface roughness is likely a first-order driver of the strong contrast in tornado potential between North and South America.

Spatial downscaling of rainfall from general circulation models: A chaos theory-based framework

The present study proposes a new chaos theory-based local approximation technique for the spatial downscaling of rainfall from GCM outputs. The approach involves: (1) reconstruction of relevant climate data in the state space using an embedding technique; and (2) use of a neighbor search procedure in the reconstructed space and local approximation for downscaling. The approach is implemented for downscaling of monthly rainfall at a spatial resolution of 0.25° × 0.25° across India. The effectiveness of the approach is first demonstrated using the NCEP/NCAR reanalysis dataset (1979–2014) and then tested using the outputs from five CMIP6 GCMs (CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1) (1961–2014). Eight climate variables (eastward wind, northward wind, relative humidity, specific humidity, surface temperature, air temperature, sea level pressure, and geopotential height) are considered as predictors, with the observed rainfall data from the India Meteorological Department (IMD) as the predictand. Three statistical measures, namely correlation coefficient (CC), Nash-Sutcliffe efficiency (NSE), and normalized root mean square error (NRMSE), are used for evaluation. The results indicate that the local approximation downscaling approach yields promising results for both reanalysis dataset and GCM outputs. Among the five GCMs, the CMCC-ESM2 model yields the best results. Comparison of the results from the chaos theory-based approach with those of the linear regression-based approach indicates better performance of the former. The proposed framework is general and can be applied for any climate variable, GCM, and region.

Assessment of CMIP6 GCMs for selecting a suitable climate model for precipitation projections in Southern Thailand

The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), quantile mapping (QM), and empirical quantile mapping (EQM). Utilizing precipitation data from 30 observation stations across Southern Thailand, the evaluation encompasses five CMIP6 GCM models (CAMS-CSM1-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, IPSL-CM6A-LR). Evaluation metrics, root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation (r), index of agreement (d), and mean bias error (MBE) are employed to assess BC methods. Evaluation measures suggest that the DT method outperforms EQM and QM, with higher accuracy and lower errors (DT: RMSE = 3.61, MAE = 2.32; EQM: RMSE = 3.82, MAE = 3.70). Taylor diagrams show that CNRM-ESM2-1 has the highest correlation across sites (r = 0.36), albeit with a wider dispersion, while CanESM5 has a more balanced performance. Significant annual precipitation increases are projected for different spans 2021-30, 2061-70, and 2091–2100, particularly under SSP585, which will influence flood risk, water management, and climate adaptation. Future projections with SSP585 continuously projecting a larger probability of increased precipitation. The DT method is recommended for the downscaling of CMIP6 GCMs for precipitation projections in Southern Thailand, recognizing its superior performance. The study's findings provide a foundation for informed decision-making and adaptation planning in southern Thailand, urging policymakers to prioritize climate resilience and adaptation strategies while using a multi-model ensemble approach for robust climate forecasts.

Assessing climate change impacts on irrigation water requirements in the Lower Mahanadi Basin: A CMIP6-based spatiotemporal analysis and future projections

ABSTRACT Climate change heightens India's agricultural risks, particularly in nations like India heavily reliant on farming. Previous studies focused on Coupled Model Intercomparison Project Phase (CMIP3) and (CMIP5) scenarios for large river basins, but the heightened risk of local climate changes poses a significant threat to smaller basins, notably affecting crops. This study investigates the spatiotemporal dynamics of climate change impacts on paddy crop irrigation in India's Lower Mahanadi Basin, utilizing the latest general circulation models (GCMs) from the CMIP6, focuses on two emission scenarios, SSP585 and SSP370. Thirteen models were analysed, top six were selected based on statistical criteria like PBIAS, NSE, R2, RSR, and RMSE. Models project climate changes for near- (2025–2050), mid- (2051–2075), and far-future (2076–2100) periods against a baseline (1981–2014), investigating spatiotemporal variations in rainfall, temperature, and irrigation water requirements (IWRs) in the region. In both scenarios, future mean seasonal rainfall is expected to increase compared with the baseline. SSP370 projects a 23.7% rise in minimum rainfall, while maximum rainfall varies by 11.5%. SSP585, on the other hand, projects a 9.53% decrease in maximum IWR and a 28.9% increase in maximum rainfall compared with the baseline. Both scenarios anticipate a 3–4 °C temperature increase in the far-future.

Crucial role of sea surface temperature warming patterns in near-term high-impact weather and climate projection

Recent studies indicate that virtually all global climate models (GCMs) have had difficulty simulating sea surface temperature (SST) trend patterns over the past four decades. GCMs produce enhanced warming in the eastern Equatorial Pacific (EPAC) and Southern Ocean (SO) warming, while observations show intensified warming in the Indo-Pacific Warm Pool (IPWP) and slight cooling in the eastern EPAC and SO. Using Geophysical Fluid Dynamics Laboratory’s latest higher resolution atmospheric model and coupled prediction system, we show the model biases in SST trend pattern have profound implications for near-term projections of high-impact storm statistics, including the frequency of atmospheric rivers (AR), tropical storms (TS) and mesoscale convection systems (MCS), as well as for hydrological and climate sensitivity. If the future SST warming pattern continues to resemble the observed pattern from the past few decades rather than the GCM simulated/predicted patterns, our results suggest (1) a drastically different future projection of high-impact storms and their associated hydroclimate changes, especially over the Western Hemisphere, (2) a stronger global hydrological sensitivity, and (3) substantially less global warming due to stronger negative feedback and lower climate sensitivity. The roles of SST trend patterns over the EPAC, IPWP, SO, and the North Atlantic tropical cyclone Main Development Region (AMDR) are isolated, quantified, and used to understand the simulated differences. Specifically, SST trend patterns in the EPAC and AMDR are crucial for modeled differences in AR and MCS frequency, while those in the IPWP and AMDR are essential for differences in TS frequency over the North Atlantic.

Development of Ridge Ensemble Standardized Drought Index (RESDI) for improving drought characterization and future assessment.

Global warming upsets the environmental balance and leads to more frequent and severe climatic events. These extreme events include floods, droughts, and heatwaves. These widespread extreme events disrupt various sectors of ecosystems directly. However, among all these events, drought is one of the most prolonged climatic events that significantly destroys the ecosystem. Therefore, accurate and efficient assessment of droughts is necessary to mitigate their detrimental impacts. In recent years, several drought indices based on global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) have been proposed to quantify and monitor droughts. However, each index has its advantages and limitations. As each index ensembles different models by using different statistical approaches, it is well known that the margin of error is always a part of statistics. Therefore, this study proposed a new drought index to reduce the uncertainty involved in the assessment of droughts. The proposed index named the Ridge Ensemble Standardized Drought Index (RESDI) is based on the innovative ensemble approach termed ridge parameters and distance-based weighting (RDW) scheme. And the development of this RDW scheme is based on two types of methods i.e., ridge regression and divergence-based method. In this research, we ensemble 18 different GCMs of CMIP6 using the RDW scheme. A comparative analysis of the RDW scheme is performed against the simple model average (SMA) and Bayesian model averaging (BMA) schemes at 32 locations on the Tibetan plateau. The comparison revealed that RDW has less mean absolute error (MAE) and root-mean-square error (RMSE). Therefore, the developed RESDI based on RDW is used to project drought properties under three distinct shared socioeconomic pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, across seven different time scales (1, 3, 7, 9, 12, 24, and 48). The projected data is then standardized by using the K-components Gaussian mixture model (K-CGMM). In addition, the study employs steady-state probabilities (SSPs) to determine the long-term behavior of drought. The outcome of this research shows that "normal drought (ND)" has the highest probability of occurrence under all scenarios and time scales.

Seafloor roughness reduces melting of East Antarctic ice shelves

Heat delivered by the ocean circulation is melting the Antarctic ice sheet from below. This melt is largest where warm Circumpolar Deep Water accesses the continental shelf and reaches the ice shelf cavities. Future melt rate projections are based on ocean thermal forcing derived from climate models, which tend to be biased warm around Antarctica. The bias has been attributed to unresolved ocean processes that occur at scales poorly represented in models. Using a high-resolution model of the Denman Glacier region we show that seafloor roughness unresolved in climate models suppresses the impact of warm water on ice sheet melting. Seafloor roughness slows down the shelf circulation, reducing the presence of warm water over the shelf and the heat transport towards the ice cavities. As a result, the total meltwater discharge drops by 4 Gt year−1. Our results suggest a mechanism missing in global ocean and climate models that could reduce the spread in climate projections.

Past and future joint return period of precipitation extremes over South Asia and Southeast Asia

Climate change is one of the major reasons for the increased intensity and frequency of precipitation extremes in tropical regions. This will have a significant impact on underdeveloped countries in South Asia and Southeast Asia. Therefore, this study analyzes changes in spatiotemporal patterns and the joint behavior of precipitation extremes across South Asian and Southeast Asian countries for the historical period (1975 to 2014) and future periods (2021–2060 (F1) and 2061–2100 (F2)). This study develops bias-corrected precipitation data and ranks General Circulation Models (GCMs) using the Empirical Quantile Mapping method and TOPSIS method, respectively. A multi-model ensemble of precipitation extremes is created using the top five GCMs. The Mann Kendall test is used to analyze trends in precipitation extremes. Joint probabilistic analysis is also conducted for different combinations of ETCCDI precipitation-based indices using Archimedes and Elliptical copulas. The results of the trend analysis indicate significant positive trends for R20mm (19.26%), R95pTOT (18.40%), Rx5Day (11.53%), and CWD (10.46%), while CDD (5.07%) shows a significant negative trend across the study area during historical period (1975–2014). The results of R20mm, R95pTOT, and Rx5Day under SSP585 shows almost 19 to 39% of area comes under significant positive trend during the F1 period. This becomes more evident in F2, with trends under SSP585 rising between 32% and 61%. The high-emissions scenario SSP585 reveals an increase in these positive trends compared to SSP126 in both the future periods (F1 and F2). These results indicating a noticeable rise in extreme precipitation events. The analysis of changes in the joint return period of precipitation extremes indicates that the South Peninsular India, North East India, West Central India and Central Northeast India, as well as most parts of Southeast Asian countries, will experience very heavy intensify precipitations in the future. In addition, persistent co-occurrence of dry and wet conditions will be observed in Pakistan, North West India, and Central Northeast India. This study provides useful information on the distribution of precipitation extremes in different regions of the study area.

Historical sensible-heat-flux variations key to predicting future hydrologic sensitivity

Under anthropogenic climate change (CC), the global hydrological cycle intensifies at a rate known as hydrologic sensitivity (HS). Global climate models (GCMs) exhibit substantial uncertainty in HS. Past work suggests that another form of HS, derived from internal climate variability (IV), is useful for constraining this uncertainty. However, these two forms of HS are weakly related. Here we show that decomposing HS under both CC and IV, based on the global energy budget, provides insight into the likely range of future HS. We find that sensible heat exchange between the atmosphere and ocean is not accounted for in the atmospheric energy budget under IV, masking the connection between HS under IV and CC. Removing this term, a closer relationship emerges. We use observations in conjunction with this relationship to suggest an upward shift in the likely range of future HS (66% confidence interval: 2.00–2.36 W m−2 K−1).

Global variability in atmospheric new particle formation mechanisms

A key challenge in aerosol pollution studies and climate change assessment is to understand how atmospheric aerosol particles are initially formed1,2. Although new particle formation (NPF) mechanisms have been described at specific sites3–6, in most regions, such mechanisms remain uncertain to a large extent because of the limited ability of atmospheric models to simulate critical NPF processes1,7. Here we synthesize molecular-level experiments to develop comprehensive representations of 11 NPF mechanisms and the complex chemical transformation of precursor gases in a fully coupled global climate model. Combined simulations and observations show that the dominant NPF mechanisms are distinct worldwide and vary with region and altitude. Previously neglected or underrepresented mechanisms involving organics, amines, iodine oxoacids and HNO3 probably dominate NPF in most regions with high concentrations of aerosols or large aerosol radiative forcing; such regions include oceanic and human-polluted continental boundary layers, as well as the upper troposphere over rainforests and Asian monsoon regions. These underrepresented mechanisms also play notable roles in other areas, such as the upper troposphere of the Pacific and Atlantic oceans. Accordingly, NPF accounts for different fractions (10–80%) of the nuclei on which cloud forms at 0.5% supersaturation over various regions in the lower troposphere. The comprehensive simulation of global NPF mechanisms can help improve estimation and source attribution of the climate effects of aerosols.

Climate Change Scenarios for Air and Water Temperatures in the Upper San Francisco Estuary: Implications for Thermal Regimes and Delta Smelt

Climate projections and their effects have been evaluated in the San Francisco Estuary as part of the U.S. Geological Survey’s CASCaDE2 project. Understanding the ecological effects of climate change can help manage and maintain the ecological health and productivity of the San Francisco Estuary. In this study, we assessed downscaled air temperature data from 10 global climate models (GCMs) under two representative concentration pathway (RCP) trajectories for greenhouse gas concentrations for three regions of the San Francisco Estuary: Suisun and Grizzly Bays, Suisun marsh, and the Sacramento-San Joaquin Delta. We also used previously derived regression models to estimate future water temperatures at 16 locations in the upper San Francisco Estuary. We used a thermal regime approach to summarize water temperature projections to investigate changes to the thermal regime of the upper San Francisco Estuary and used the Delta Smelt (Hypomesus transpacificus) to demonstrate the impacts that a warming climate may have on the habitat needs of this fish species. Our results suggested there were no major differences in the extent of air temperature warming among the three regions. Annual average air temperatures were projected to increase approximately 2.0 and 4.7°C by the end of the century for the low and high RCP scenarios, respectively. We found timing, frequency, and magnitude metrics varied by period and RCP scenario, while duration and variability metrics varied by space for water temperature thermal regimes. For example, the spawning window for Delta Smelt (thermal regime duration metric) is projected to expand in the future, with spawning starting earlier for both RCP scenarios for most sites. Although our thermal regime analysis focused on the life history of Delta Smelt, similar approaches could be used to assess climate change threats to a wide array of native and invasive terrestrial and aquatic species found in San Francisco Estuary.

Evaluating the hydrological regime alteration under extreme climate scenarios in Southeast China

Study regionThe Shanmei Reservoir Watershed (SRW), Southeast China. Study focusThis study investigated the climate and hydrological regimes alterations in a subtropical coastal watershed (SRW) during the 21st century in extreme scenarios. The extreme scenarios, i.e., warm-wet and cold-dry climates, were constructed using 18 global climate models (GCMs) from CMIP6 under shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5). The Soil and Water Assessment Tool (SWAT) model and Indicators of Hydrologic Alteration (IHA) were applied to quantify the impacts of climate change on the eco-hydrological regimes during the projected period (2041–2100) compared to the base period (1980–2014). New hydrological insights for the regionThe results show that the average temperature rises by 0.6–3.8 ℃, and the average annual precipitation changes by −21.4 % - 32.4 % by the end of the 21st century under extreme scenarios. Contrasting hydrological regimes are expected in the SRW under extreme scenarios. Under the extreme warm-wet scenarios, the monthly runoff is lower during spring and higher during summer, the minimum flows are significantly higher, and the maximum and minimum flows occurs earlier. Water resource utilization and ecosystem health are expected to improve. However, the opposite holds true in the cold-dry scenarios. The hydrologic regime alteration under future extreme climate scenarios can guide local water planning and ecological restoration strategies.

The temperature feedback in the intraseasonal Warm Arctic–Cold North America pattern

The feedback mechanism between air temperature (Ta) and skin temperature (Ts) and its origin is disclosed for the intraseasonal warm Arctic-cold North America pattern based on reanalysis data and simulations from an atmospheric global climate model. The change in Ta is caused by an anomalous anticyclonic circulation over Alaska and the resultant meridional temperature advection. It changes Ts through downward longwave radiation (DLWR), and the altered Ts further feedback to Ta via upward longwave radiation. While these processes decay the initial changes in Ta, they are important for the feedback between Ta and Ts. Compared with clouds, atmospheric moisture and the associated clear-sky DLWR dominate the changes in DLWR. Moisture thus plays a crucial role in the strength of the Ts-Ta feedback.