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The representation of groundwater dynamics in land surface models and their roles in global precipitation variations has received attention in recent years. Studies have revealed the overall higher soil moisture but rather diverse precipitation changes after incorporating the groundwater component in climate models. However, groundwater effects on large-scale atmospheric energy transport, the fundamental atmospheric variable regulating Earth’s climate, have not been explored thoroughly. In this study, a pair of idealized experiments corresponding to contrast globally fixed water table depths by AMIP-type simulations in the Community Earth System Model was conducted. In the wet (shallow water table) experiments, an increased meridional surface temperature gradient makes the mean meridional energy transports and Hadley circulation stronger than dry (deep water table) experiments over the tropics. Such energy transport changes are primarily attributed to the dynamic contribution (intensified Hadley circulation). The wet experiments make the simulated world be like an aquaplanet simulation with less land–sea temperature contrast and the enhancement (reduction) of mean meridional circulation (stationary eddies) energy transports. Furthermore, the South Asian monsoon circulation in the wet experiment shows a southward shift in the premonsoon season (April–June) and slight weakening in the mature phase (July and August). This study explores the impacts of the soil conditions caused by various water table depths on global energy transport and has further implications for climate model developments and experiment designs.

As the third most important greenhouse gas (GHG) after carbon dioxide (CO2) and methane (CH4), tropospheric ozone (O3) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O3 depletion in the springtime; however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been previously reported, and we show that there continues to be no convergence among models or improved accuracy in simulating tropospheric O3 and its precursor species. The MMM underestimates Arctic surface O3 by 5 % to 15 % depending on the location. The vertical distribution of tropospheric O3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O3 within a range of ±50 % depending on the model and the altitude. The MMM performs best, within ±8 % for most locations and seasons. However, nearly all models overestimate O3 near the tropopause (∼300 hPa or ∼8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O3 at high latitudes. For example, the MMM is biased high by about 20 % at Eureka. Observed and simulated O3 precursors (CO, NOx, and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NOx but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O3 matches the observed O3 reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever and have not increased in accuracy for representing Arctic tropospheric O3.

As the third most important greenhouse gas (GHG) after CO2 and methane, tropospheric ozone (O3) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O3 depletion in the springtime, however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been reported previously, and we show that there continues to be no convergence among models, nor improved accuracy in simulating tropospheric O3 and its precursor species. The MMM underestimates Arctic surface O3 by 5 % to 15 % depending on the location. The vertical distribution of tropospheric O3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O3 within a range of +/- 50 % depending on the model and the altitude. The MMM performs best, within +/- 8 % at most locations and seasons. However, nearly all models overestimate O3 near the tropopause (~300 hPa or ~8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O3 at high latitudes. For example, the MMM is biased high by about 20 % at Eureka. Observed and simulated O3 precursors (CO, NOx and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NOx but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O3 matches the observed O3 reasonably well. Our findings suggest that despite model updates over the last decade , model results are as highly variable as ever, and have not increased in accuracy for representing Arctic tropospheric.

The Arctic atmosphere is warming rapidly and its relatively pristine environment is sensitive to the long-range transport of atmospheric pollutants. While carbon dioxide is the main cause for global warming, short-lived climate forcers (SLCFs) such as methane, ozone, and particles also play a role in Arctic climate on near-term time scales. Atmospheric modelling is critical for understanding the abundance and distribution of SLCFs throughout the Arctic atmosphere, and is used as a tool towards determining SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models, assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over four years (2008–2009 and 2014–2015) conducted for the 2021 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship and aircraft-based observations. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean was able to represent the general features of SLCFs in the Arctic, though vertical mixing, long-range transport, deposition, and wildfire emissions remain highly uncertain processes. These need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment.

Tropical deforestation can result in substantial changes in local surface energy and water budgets, and thus in atmospheric stability. These effects may in turn yield changes in precipitation. The Maritime Continent (MC) has undergone severe deforestation during the past few decades but it has received less attention than the deforestation in the Amazon and Congo rain forests. In this study, numerical deforestation experiments are conducted with global (i.e., Community Earth System Model) and regional climate models (i.e., Regional Climate Model version 4.6) to investigate precipitation responses to MC deforestation. The results show that the deforestation in the MC region leads to increases in both surface temperature and local precipitation. Atmospheric moisture budget analysis reveals that the enhanced precipitation is associated more with the dynamic component than with the thermodynamic component of the vertical moisture advection term. Further analyses on the vertical profile of moist static energy indicate that the atmospheric instability over the deforested areas is increased as a result of anomalous moistening at approximately 800–850 hPa and anomalous warming extending from the surface to 750 hPa. This instability favors ascending air motions, which enhance low-level moisture convergence. Moreover, the vertical motion increases associated with the MC deforestation are comparable to those generated by La Niña events. These findings offer not only mechanisms to explain the local climatic responses to MC deforestation but also insights into the possible reasons for disagreements among climate models in simulating the precipitation responses.

A comprehensive estimation of water budget components, particularly groundwater storage (GWS) and fluxes, is crucial. In this study, we evaluate the terrestrial water budget of the Donga basin (Benin, West Africa), as simulated by three land surface models (LSMs) used in the African Monsoon Multidisciplinary Analysis Land Surface Model Intercomparison Project, phase 2 (ALMIP2): CLM4, Catchment LSM (CLSM), and Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO). All three models include an unconfined groundwater component and are driven by the same ALMIP2 atmospheric forcing from 2005 to 2008. Results show that all three models simulate substantially shallower water table depth (WTD) with smaller seasonal variations, approximately 1–1.5 m compared to the observed values that range between 4 and 9.6 m, while the seasonal variations of GWS are overestimated by all the models. These seemingly contradictory simulation results can be explained by the overly high specific yield prescribed in all models. All models achieve similar GWS simulations but with different fractions of precipitation partitioning into surface runoff, base flow, and evapotranspiration (ET), suggesting high uncertainty and errors in the terrestrial and groundwater budgets among models. The poor performances of models can be attributed to bias in the hydrological partitioning (base flow vs surface runoff) and sparse subsurface data. This analysis confirms the importance of subsurface hydrological processes in the current generation of LSMs and calls for substantial improvement in both surface water budget (which controls groundwater recharge) and the groundwater system (hydrodynamic parameters, vertical geometry).