Atmospheric General Circulation Model Research Articles

Diagnosing the Factors That Contribute to the Intermodel Spread of Climate Feedback in CMIP6

Abstract The global surface temperature climate feedback parameter λ varies significantly across climate models, and its real-world value remains uncertain. Studies have found that the sea surface temperature (SST) response pattern and atmospheric model physics can each affect the climate feedback parameter in historical and idealized warming simulations. In this study, we design and analyze a series of the targeted atmospheric global climate model (AGCM) experiments to quantify how much the SST (both the warming pattern and the base climatology) and atmospheric model physics contributes to the intermodel spread of the climate feedback parameter and cloud feedback in phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use three AGCMs, HiRAM, AM2.5, and AM4, developed in the Geophysical Fluid Dynamics Laboratory (GFDL), which span a wide range of feedbacks in response to uniform surface warming. The three GFDL models indicate that historical patterns of SST change systematically alter λ, supporting the hypothesized role for a “pattern effect” in historical climate sensitivity. However, we found that forcing one AGCM with the different CO2-induced SST warming patterns or model SST climatology from a suite of climate models from CMIP6 can only reproduce ∼10% the intermodel spread of CO2-induced λ, while the atmospheric model used determines the magnitude of λ (∼45%). This underscores the role of atmospheric model physics in altering λ, particularly the cloud-related schemes. In addition, we demonstrate that the nonlinear interaction between SST and AGCM has a nonnegligible role in affecting λ.

Hot Jupiters Are Asynchronous Rotators

Abstract Hot Jupiters are typically assumed to be synchronously rotating, from tidal locking. Their thermally driven atmospheric winds experience Lorentz drag on the planetary magnetic field anchored at depth. We find that the magnetic torque does not integrate to zero over the entire atmosphere. The resulting angular momentum feedback on the bulk interior can thus drive the planet away from synchronous rotation. Using a toy tidal–ohmic model and atmospheric general circulation model outputs for HD189733b, HD209458b, and Kepler7b, we establish that off-synchronous rotation can be substantial at tidal–ohmic equilibrium for sufficiently hot and/or magnetized hot Jupiters. Potential consequences of asynchronous rotation for hot Jupiter phenomenology motivate follow-up work on the tidal–ohmic scenario with approaches that go beyond our toy model.

Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times.

During the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident on March 11, 2011, radionuclides such as tritium were released into the environment across Japan, obscuring the natural background signal of tritium in precipitation. This anthropogenic component was rapidly washed out by precipitation according to measurements in Japan. However, the impact of the accident on the natural tritium-based estimation of water system transit times in Fukushima and other prefectures in Japan remains uncertain. For the first time, anthropogenic tritium from the FDNPP accident together with natural tritium were simulated in an atmospheric general circulation model with a good ability to represent tritium variations in daily and monthly precipitation. For the FDNPP accident, we estimate the maximum tritium atmospheric emission of 0.815 PBq with a tritium in precipitation peak of 68.7 Bq/L (582 tritium units) on March 2011 at Fukushima, which are consistent with previous estimations. Using our modeled outputs with tritium measurements, we improve tritium-tracer application for estimating mean transit times of Fukushima surface and groundwater systems impacted by the anthropogenic tritium from the FDNPP accident. While the anthropogenic impact of the FDNPP accident was limited compared to the tritium peak due to the thermonuclear testing, globally modeled tritium in precipitation is useful to apply for other areas of tritium-tracer studies.

The JAGUAR-DAS whole neutral atmosphere reanalysis: JAWARA

Using the Japanese Atmospheric General circulation model for the Upper Atmosphere Research-Data Assimilation System (JAGUAR-DAS), a whole neutral atmosphere reanalysis dataset (JAWARA) over about 19 years from September 2004 to December 2023 is produced. JAWARA is the first long-period reanalysis covering the height region from the surface to the lower thermosphere (~ 110 km). This wide height coverage is a notable advantage over other operational reanalysis datasets, which cover up to the middle mesosphere. Key dynamical characteristics are compared between JAWARA and two satellite observations and three other operational reanalysis datasets in their covered height regions. The seasonal variations of zonal mean temperature and zonal wind are similar between JAWARA and the datasets used for comparison. The climatologies of zonal mean temperature, zonal wind, residual-mean circulation, and E-P flux in the meridional cross section are also broadly consistent with other reanalysis datasets. The analyzed residual-mean vertical flow in the northern high latitudes in the middle atmosphere exhibits the well-known patterns of upwelling in summer and downwelling in winter. JAWARA also shows a prominent feature of strong downward propagating anomalies from the lower thermosphere to the upper stratosphere after sudden stratospheric warmings. This analysis takes full advantage of the JAWARA data, which cannot be made using satellite observations and other reanalysis datasets. This reanalysis product is expected to contribute broadly to future research on the characteristics of observed mesospheric phenomena, thermosphere–ionosphere coupling, space weather, and improvement of middle atmospheric meteorological systems, including their interannual and decadal scale variability.

Wave climate projections off coastal French Guiana based on high-resolution modelling over the Atlantic Ocean

Global warming is altering the atmosphere and ocean dynamics worldwide, including patterns in the generation and propagation of ocean waves, which are important drivers of coastal evolution, flood risk, and renewable energy. In French Guiana (northern South America), where most of the population is concentrated in coastal areas, understanding future wave climate change is critical for regional development, planning and adaptation purposes. The most energetic waves typically occur in boreal winter, in the form of long-distance swell originating from the mid-latitude North Atlantic Ocean. However, existing high-resolution wave climate projections that cover the French Guiana region focus on the hurricane season only (summer-fall). In this study, we used a state-of-the-art basin-scale spectral wave model and wind fields from a high-resolution atmospheric global climate model to simulate present and future winter (November to April) wave climate offshore of French Guiana. The model performance was evaluated against wave data from ERA5 reanalysis, satellite altimetry and coastal buoys between 1984 and 2013. For the future greenhouse gas emission scenario (Representative Concentration Pathway) RCP-8.5, we found a statistically significant overall projected decrease (∼5 %) in wintertime average significant wave height and mean wave period, with a ∼1° clockwise rotation of mean wave direction. The results suggest that these decreasing trends are primarily driven by changes in large-scale patterns across the Atlantic that counteract an expected increase in local wind speed. We discuss the implications of such projections for mud-bank dynamics along coastal French Guiana, although further local studies are required to address future coastal evolution and hazards. Finally, we identify a need for more in situ wave data near French Guiana to improve quantitative assessments of model performance and allow a correction of possible model biases.

An intelligent deep learning-based approach for downscaling atmospheric general circulation model outputs

An intelligent deep learning-based approach for downscaling atmospheric general circulation model outputs

Embedding machine-learnt sub-grid variability improves climate model precipitation patterns

Parameterisation schemes within General Circulation Models are required to capture cloud processes and precipitation formation but exhibit long-standing known biases. Here, we develop a hybrid approach that tackles these biases by embedding a Multi-Output Gaussian Process trained to predict high resolution variability within each climate model grid box. The trained multi-output Gaussian Process model is coupled in-situ with a simplified Atmospheric General Circulation Model named SPEEDY. The temperature and specific humidity profiles of SPEEDY are perturbed at fixed intervals according to the variability predicted from the Gaussian Process. Ten-year predictions are generated for both control and machine learning hybrid models. The hybrid model reduces the global precipitation area-weighted root-mean squared error by up to 17% and over the tropics by up to 20%. Hybrid techniques have been known to introduce non-physical states therefore physical quantities are explored to ensure that climatic drift is not observed. Furthermore, to understand the drivers of the precipitation improvements the changes to thermodynamic profiles and the distribution of lifted index values are investigated.

Stronger Westerly Wind Bursts in a Warming Climate: The Effects of the Pacific Warming Pattern, the Madden–Julian Oscillation, and Tropical Cyclones

Abstract Westerly wind bursts (WWBs) in the western–central equatorial Pacific are critical in El Niño–Southern Oscillation (ENSO) dynamics. Understanding how they may change with global warming has important implications for future projections of El Niño. In this study, we investigate how the enhanced eastern equatorial Pacific warming pattern, emerging in future climate projections, can influence WWB characteristics in an atmospheric general circulation model, CAM6. Changes in three main factors affecting WWBs—El Niño-conditions mean westerly wind stress anomalies, the Madden–Julian oscillation (MJO), and tropical cyclones (TCs)—are analyzed. We find that during El Niño onset (December–April), the WWB wind stress intensity remains largely unchanged but WWBs shift westward by about 20° of longitude. In contrast, during El Niño development (May–November), the WWB intensity increases by 41% or 79% in two warming scenarios considered [shared socioeconomic pathways (SSP5-8.5) and SSP2-4.5, respectively], which is mainly caused by a higher frequency of TC occurrence in the central tropical Pacific within 15°N/S. Further, we find that westerly wind speed anomalies associated with El Niño and the MJO also increase during El Niño development. However, as trade winds weaken in the central-eastern equatorial Pacific, the mean strength of the resulting westerly wind stress anomalies does not change much, and no significant eastward shift of WWBs is observed. Thus, our atmospheric model simulations suggest a strong TC-driven increase in WWB wind stress anomalies during El Niño development and hence a more important role of TCs in WWB generation, which in a coupled system could lead to stronger ENSO events, enhanced TC–ENSO coupling, and even greater intensification of WWBs.

Sensitivity of the Northern Hemisphere Warming Trend to Snowpack Variability

Abstract A marked decrease in land surface snow cover within the Northern Hemisphere under global warming will warm the atmosphere near the land surface. However, minimal information exists regarding the contribution of snowpack variation to interannual surface air temperature variability. The current study investigates the effects caused by snow water equivalent (SWE) change on surface air temperature (SAT). To this end, an SWE pacemaker experiment of land–atmosphere coupling (AMIP-type) is undertaken for the period 1901–2010, during which the Model for Interdisciplinary Research on Climate, version 6 (MIROC6), atmospheric general circulation model’s SWE is continuously nudged toward SWE derived from an land-only (LMIP-type) experiment. Compared to a reference MIROC6 simulation without SWE nudging, the spatial correlation of 1980–2010 interannual SWE trends in our experiment with those in GlobSnow observation data increased by 0.31 over the Northern Hemisphere. Similarly, the linear correlation of interannual SAT with reanalysis data is greater by 0.04, 0.04, 0.08, and 0.07 for autumn, winter, spring, and summer over the region from the reference experiment, respectively. It is shown that due to this SWE nudging, the modeled interannual SAT change in the Northern Hemisphere becomes more accurate. Through a surface energy budget analysis, changes in SAT are attributed to changes in surface albedo, soil evaporation, and soil temperature. Areas of greater contribution of SWE variability to SAT variability appear to shift from south to north areas as snow melts. These results highlight surface albedo, snow hydrological, and land heat storage effects through which the SWE interannual trend on the land surface significantly controls atmospheric air temperature variability near the land surface. Significance Statement This study investigates the contribution of land snowpack to surface air temperature in the Northern Hemisphere using a climate model updated with observation-based estimates of snow water equivalent. A marked decrease in snowpack under global warming is expected to warm the atmosphere near the land surface. Variations in surface albedo, soil evaporation, and soil temperature through the snow–soil–atmosphere processes during the early snow-melting season are crucial for accurately simulating surface air temperature in the Northern Hemisphere. Moreover, terrestrial snowpack is a significant factor in global warming rates.

Impact of biomass burning aerosols (BBA) on the tropical African climate in an ocean–atmosphere–aerosol coupled climate model

Abstract. The impact of biomass burning aerosols (BBA) emitted in central Africa on the tropical African climate is studied using the ocean–atmosphere global climate model CNRM-CM, including prognostic aerosols. The direct BBA forcing, cloud feedbacks (semi-direct effects), effects on surface solar radiation, atmospheric dynamics and precipitation are analysed for the 1990–2014 period. During the June–July–August (JJA) season, the CNRM-CM simulations reveal a BBA semi-direct effect exerted on low-level clouds with an increase in the cloud fraction of ∼5 %–10 % over a large part of the tropical ocean. The positive effect of BBA radiative effects on low-level clouds is found to be mainly due to the sea surface temperature response (decrease of ∼0.5 K) associated with solar heating at 700 hPa, which increases the lower-tropospheric stability. Over land, results also indicate a positive effect of BBA on the low-cloud fraction, especially for the coastal regions of Gabon and Angola, with a potentially enhanced impact in these coupled simulations that integrates the response (cooling) of the sea surface temperature (SST). In addition to the BBA radiative effect on SST, the ocean–atmosphere coupled simulations highlight that the oceanic temperature response is noticeable (about −0.2 to −0.4 K) down to ∼80 m depth in JJA between the African coast and 10° W. In parallel to low-level clouds, reductions of ∼5 %–10 % are obtained for mid-level clouds over central Africa, mainly due to BBA-induced surface cooling and lower-tropospheric heating inhibiting convection. In terms of cloud optical properties, the BBA radiative effects induced an increase in the optical depth of about ∼2–3 over the ocean south of the Equator. The result of the BBA direct effect and feedback on tropical clouds modulates the surface solar radiation over the whole of tropical Africa. The strongest surface dimming is over central Africa (∼-30 W m−2), leading to a large reduction in the continental surface temperature (by ∼1 to 2 K), but the solar radiation at the oceanic surface is also affected up to the Brazilian coast. With respect to the hydrological cycle, the CNRM-CM simulations show a negative effect on precipitation over the western African coast, with a decrease of ∼1 to 2 mm d−1. This study also highlights a persistent impact of BBA radiative effects on low-level clouds (increase in cloud fraction, liquid water content and optical depth) during the September–October–November (SON) period, mainly explained by a residual cooling of sea surface temperature over most of the tropical ocean. In SON, the effect on precipitation is mainly simulated over the Gulf of Guinea, with a reduction of ∼1 mm d−1. As for JJA, the analysis clearly highlights the important role of the slow response of the ocean in SON and confirms the need to use coupled modelling platforms to study the impact of BBA on the tropical African climate.

Intraseasonal relationship of winter temperatures in North America and warm sea surface temperatures in the Northeast Pacific

Winter temperatures in the North America (NA) exhibit evident intraseasonal variation, which has become more pronounced under global climate change. In particular, the wintertime relationship between NA temperatures and long-lasting positive sea surface temperature anomalies (SSTAs) in the Northeast Pacific (NEP), known as warm blobs, has been hotly debated in recent years. However, their relationship on intraseasonal timescales remains unclear. Using reanalysis data and an Atmospheric General Circulation Model, this study reveals that the intraseasonal variation of atmospheric circulation is the primary factor to induce intraseasonal temperature anomalies in NA after classifying warm SSTA events based on their different evolutions. Moreover, the atmospheric circulation anomalies over the mid-to-high-latitude regions are in part contributed by the feedback of NEP warm SSTAs to the atmosphere. Specifically, for early-winter warm SSTA events (peaking in November), there are two intraseasonal reversals in geopotential height anomalies over the NEP and NA, corresponding to a “cold-warm-cold” temperature change in NA. However, for mid-winter warm SSTA events (peaking in January), the atmospheric circulation pattern is relatively stable, mainly characterized by a “west high-east low” dipole, associated with persistent cold anomalies in NA. Then, the role of warm SSTAs over the NEP in the intraseasonal temperature variation is generally verified by the numerical simulations. Hence, although the NEP warm SSTAs may not be the dominant driver of the intraseasonal variations of winter NA temperatures, we emphasize the implication of their evolutionary differences in reflecting the NA temperature variations, which contributes to better predictability.

Impacts of Local and Remote SST Warming on Summer Circulation Changes in the Western North Pacific

Abstract This study explores how future SST warming in remote ocean basins may affect the western North Pacific (WNP) wet season climate by applying a high-resolution atmospheric general circulation model to conduct a series of numerical experiments. A marked precipitation and tropical cyclone (TC) activity reduction, as well as enhanced anticyclonic circulation, in the WNP is projected in AMIP experiments forced by SST change in a future warming scenario. The sensitivity experiments reveal that various SST warming phenomena (e.g., in the global SST warming pattern, the tropical ocean belt, the Indian Ocean, the tropical Atlantic, and the subtropical northeast Pacific) and the increase in greenhouse gas concentration could weaken the precipitation, TC activity, and circulation. By contrast, the SST warming in the WNP and eastern equatorial Pacific has opposite and mixed effects, respectively, and tends to weakly offset the dominant influences of remote ocean warming. These results indicate that the WNP, being the epicenter of the global teleconnection of divergent and rotational flow, is susceptible to the influence of the SST warming in remote ocean basins. The remote forcing as projected in future scenarios would overwhelm the enhancing effect of local SST warming and weaken the circulation, convection, and TC activity in the WNP. These findings further the understanding of how the decreased precipitation and enhanced subtropical high in the WNP may be easily triggered by remote SST warming as revealed in the AMIP-type simulations. How this effect would be affected by air–sea coupling needs further investigation.

An Empirical Parameterization of the Subgrid‐Scale Distribution of Water Vapor in the UTLS for Atmospheric General Circulation Models

AbstractTemperature and water vapor are known to fluctuate on multiple scales. In this study 27 years of airborne measurements of temperature and relative humidity from In‐service Aircraft for a Global Observing System (IAGOS) are used to parameterize the distribution of water vapor in the upper troposphere and lower stratosphere. The parameterization is designed to simulate water vapor fluctuations within gridboxes of atmospheric general circulation models (AGCMs) with typical size of a few tens to a few hundred kilometers. The distributions currently used in such models are often not supported by observations at high altitude. More sophisticated distributions are key to represent ice supersaturation, a physical phenomenon that plays a major role in the formation of natural cirrus and contrail cirrus. Here the observed distributions are fitted with a beta law whose parameters are adjusted from the gridbox mean variables. More specifically the standard deviation and skewness of the distributions are expressed as empirical functions of the average temperature and specific humidity, two typical prognostic variables of AGCMs. Thus, the distribution of water vapor is fully parameterized for a use in these models. The new parameterization reproduces the observed distributions with a determination coefficient always greater than 0.917 and with a mean value of 0.997. The parameterization is robust to a selection of various geographical subsets of data and to gridbox sizes varying between 25 and 300 km.

An Implementation of the Particle Flow Filter in an Atmospheric Model

Abstract The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high-dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an observing system simulation experiment (OSSE) in a simplified atmospheric general circulation model and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a yearlong cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future. Significance Statement Data assimilation is an important tool in weather forecasting. However, fundamental issues arising from linear and Gaussian approximations still exist, which can limit our utilization of observations. The particle flow filter is a promising method that avoids these approximations, but efficient algorithms for this method have yet to be developed. In this study, we develop an algorithm for the particle flow filter that can be implemented in high-dimensional geophysical models. We have demonstrated, for the first time, that the particle flow filter runs efficiently for a yearlong experiment in an atmospheric model. The new algorithm also improves the results over a commonly used data assimilation method. The results in this work demonstrate the potential usage of the particle flow filter in the weather forecasting and other high-dimensional forecasting problems.

Robustness of the relationship between tropical high-cloud cover and large-scale circulations

High clouds play an important role in climate variations by shading the sun and contributing to the greenhouse effect. It has been suggested that these variations are strongly controlled by atmospheric circulations; however, the relationship between high clouds and circulations in global simulations has been confirmed only for a limited number of general circulation models (GCMs). Increasing our understanding of this relationship requires a more complete dataset of state-of-the-art GCMs. This study quantifies the relationship between high clouds and circulations by spatial and temporal correlation using data from 28 atmospheric GCMs along with a global nonhydrostatic model, a new-type of GCM, and reveals a robust and strong relationship in most models. This study also finds that the sensitivity, which is defined as the slope of the relationship between high clouds and mass fluxes of circulations, is highly model-dependent. This suggests that a similar change of circulations does not guarantee that of high clouds, which could be one reason for the complexity in projecting high-cloud changes due to warming. Moreover, this study confirms in both observation and models that the relationship with circulations is stronger for medium-thickness and thick high clouds than for thin clouds. Furthermore, the relationship between high clouds and circulations is more convincing in near-equatorial regions between 15°S–15°N, the ascending branch of the Hadley circulation, where deep convection is especially active.

Present Climate and Future Changes in the Annual Cycle of TC Activity in the WNP Investigated by HighResMIP GCMs

Abstract Atmospheric general circulation models (AGCMs) and coupled general circulation models (CGCMs) in the High Resolution Model Intercomparison Project (HighResMIP) were evaluated on their ability to simulate tropical cyclone (TC) activity in the western North Pacific (WNP) over its annual cycle. Specifically, we examined these models’ ability to simulate the south–north migration of the mean TC genesis location. The results revealed that both types of models realistically captured TC numbers and the south–north migration of TC genesis locations associated with the meridional migration of the WNP subtropical high ridge in response to the annual cycle. However, TC numbers decreased less rapidly in the AGCMs than in both the CGCMs and observed data during the monsoon retreat period (after September). This bias was probably attributed to a low-tropospheric cyclonic anomaly over the Philippine Sea in response to La Niña–like sea surface temperature (SST) differences between the AGCMs and the CGCMs. Because of these differences, the TC genesis frequency in the AGCMs over the Philippine Sea was overestimated. The cyclonic anomaly occurred when the northeasterly trade wind arose and was maintained through wind–evaporation–SST feedback. In future climate (2021–50), the main changes occurred during the monsoon retreat period. A more rapid decrease in TC numbers shown in AGCMs was likely attributed to the decrease (increase) of low-level vorticity (vertical wind shear) modulated by enhanced WNP subtropical high and subsidence anomalies. Conversely, a cyclonic circulation and ascending anomalies projected by CGCMs were identified off-equator, which favored TC genesis location shifting northward. Significance Statement Model performance and future changes in the annual cycle of tropical cyclone (TC) activity in the western North Pacific are investigated. We found that high-resolution uncoupled and coupled models realistically captured the TC numbers and meridional migration of TC genesis locations associated with the meridional migration of subtropical high ridge. However, TC numbers decreased more gradually in the uncoupled models than in the coupled models after September. This lower skill may stem from differences between the uncoupled and coupled models in simulating a cyclonic anomaly over the Philippine Sea in response to the La Niña–like sea surface temperature difference. In future climate (2021–50), TC numbers are significantly projected to decrease more rapidly during the monsoon retreat period in uncoupled models.

Remote Forcing for a Circulation Pattern Favorable to Surface Melt over the Ross Ice Shelf

Abstract The Ross Ice Shelf (RIS) experiences surface melt events in summer, which could accelerate ice loss and destabilize the ice sheet in a warming world. This study links the interannual variability of RIS surface melt to the northerly wind anomaly over the Ross Sea sector, which is established in association with the quasigeostrophic barotropic Rossby wave trains from the tropical Pacific and subtropical Australia toward West Antarctica. Atmospheric general circulation model experiments suggest that these Rossby wave trains are regulated by El Niño–related sea surface temperature (SST) anomalies in the tropical central–eastern Pacific and atmospheric heating anomalies over western Australia. El Niño provides an important forcing of the atmospheric circulation anomalies over the Ross Sea via inducing a Rossby wave train, and most surface melt events over the RIS happen during El Niño years. In addition, the anomalous atmospheric heating over western Australia, which is independent of El Niño, is another important forcing that triggers a Rossby wave train extending from subtropical Australia to the Ross Sea. The northerly flow toward the Ross Sea induces strong poleward moisture and heat transport, which further contributes to surface melt over the RIS. Significance Statement During austral summer, surface melt occurs over the Ross Ice Shelf, accelerates ice loss, and poses ice-sheet destabilization risks in a warming world. The northerly wind anomaly over the Ross sector provides strong poleward heat and moisture transport and is favorable for the surface melt. This wind anomaly is influenced by two remote forcings, El Niño and heating anomaly over western Australia, through generating Rossby wave trains from the tropics and subtropical Australia. This study reveals a previously unexplored relationship that atmospheric heating over western Australia influences large-scale circulation, contributing to surface melt.

Unprecedented monsoon precipitation over southwest Pakistan in 2022: Regional processes in moistening the climatological heat low

AbstractDuring the 2022 Asian summer monsoon, the climatological driest parts of Sindh and Balochistan provinces in southwestern Pakistan and the northern Arabian Sea (regions of climatological heat low, HLOW) experienced unprecedented precipitation (>500% of the normal) whereas precipitation was reduced from the Indo‐Gangetic Plain to the tropical western Pacific. Our working hypothesis is that the weakened large‐scale monsoon is a direct response to tropical sea‐surface temperature: wave responses that develop in response to changes in diabatic heating anomalies over the regional precipitation centers within the Asian monsoon intensify and transition HLOW into an anomalous moist low. To validate the hypothesis, process‐oriented diagnostics are applied to European Centre of Medium‐range Weather Forecasts Reanalysis v5 (ERA5), and numerical experiments are performed with a linear atmospheric general circulation model. Model solutions confirm that the weakened large‐scale monsoon, essentially a linear response, is determined by persistent warm sea‐surface temperature and enhanced precipitation anomalies over the equatorial and southeastern Indian Ocean–Maritime Continent, and Rossby waves emanating from there, and from continental India, deepen the HLOW. Concomitantly, as a Rossby wave response to negative precipitation anomalies over the northern Bay of Bengal and Indochina during June, and their poleward migration during July–August, positive height anomalies develop and intensify over northern India. The resultant horizontal pressure gradient between HLOW and northern India drives concentrated low‐level wind anomalies that are efficient in advecting the strongest climatological moisture gradient to precondition the lower troposphere during June, and in determining the unprecedented precipitation during July–August when the seasonal cycle prevails over HLOW. Model sensitivity to horizontal moisture advection confirms ERA5 diagnostics. Nearly identical tropical forcing and large‐scale weakened monsoon responses are observed during 2010 and 2020. In these years, diagnostics identify subtle changes in latitudinal position of negative precipitation anomalies over the Bay of Bengal and Indo‐Gangetic Plain that lead to lesser contribution by horizontal moisture advection, resulting in weaker positive precipitation anomalies over southwest Pakistan.

South Asian summer monsoon enhanced by the uplift of the Iranian Plateau in Middle Miocene

Abstract. The South Asian summer monsoon (SASM) significantly intensified during the Middle Miocene (17–12 Ma), but the driver of this change remains an open question. The uplift of the Himalaya (HM) and the Iranian Plateau (IP) and global CO2 variation are prominent factors among suggested drivers. Particularly, the impact of high CO2 levels on the Miocene SASM has been little studied, despite the wide range of reconstructed CO2 values around this period. Here we investigate their effects on the SASM using the fully coupled Ocean–Atmosphere Global Climate Model, CESM1.2, through a series of 12 sensitivity experiments. Our simulations show that the IP uplift plays a dominant role in the intensification of the SASM, mainly in the region around northwestern India. The effect of the HM uplift is confined to the range of the HM and its vicinity, producing orographic precipitation change. The topography forcing overall out-competes CO2 variation in driving the intensification of the SASM. In the case of extremely strong CO2 variation, the effects of these two factors are comparable in the core SASM region, while in the western region, the topographic forcing is still the dominant driver. We propose a thermodynamical process linking the uplift of the IP and the enhanced SASM through the release of latent heat. When compared with reconstructions, the simulated response of SASM to the IP uplift is in good agreement with observed precipitation and wind field, while the effects of the HM uplift and CO2 variation are inadequate to interpret the proxies.

Diagnosing drivers of tropical precipitation biases in coupled climate model simulations

In this study, we analyse the large-scale biases in tropical precipitation climatology of two different types of coupled ocean–atmosphere general circulation models (GCMs): the coupled model intercomparison project (CMIP) models and ICON-Sapphire, a global storm-resolving model. We employ the simple globally resolved energy balance (GREB) diagnostic precipitation model to evaluate four drivers of the precipitation biases in the simulations: surface specific humidity, surface relative humidity, tropospheric mean and variability in the vertical motion. The tropical precipitation biases in the CMIP and ICON-Sapphire simulations are surprisingly similar in their patterns and also in the elements forcing them. The results of our analysis using the GREB model show that the precipitation biases result from both biases in the sensitivity to the four forcing fields and biases in the simulated forcing fields themselves. The most significant bias for both, the CMIP and ICON-Sapphire simulations, is a too high sensitivity to the mean vertical circulation (ωmean\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\omega }_{\ ext{mean}}$$\\end{document}) and bias in the ωmean\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\omega }_{\ ext{mean}}$$\\end{document} climatology itself. This also holds for specific long-standing biases, such as the double intertropical convergence zone problem. Meanwhile, biases in the climatology of specific and relative humidity play only a minor role but contribute to an overall small increase in precipitation in CMIP models that may be related to the “drizzling” bias. These results can give insights to the modelling community regarding model development, and illustrate that the GREB diagnostic precipitation model applied is a good tool for evaluating the drivers of large-scale tropical precipitation.