Coarse-resolution Climate Models Research Articles

Resolution Dependence of Extreme Wind Speed Projections in the Great Lakes Region

Abstract The effect of anthropogenic climate change on extreme near-surface wind speeds is uncertain. Observed trends are weak and difficult to disentangle from internal variability, and model projections disagree on the sign and magnitude of trends. Standard coarse-resolution climate models represent the fine structures of relevant physical phenomena such as extratropical cyclones (ETCs), upper-level jet streaks, surface energy fluxes, and land surface variability less skillfully than their high-resolution counterparts. Here, we use simulations with the NCAR Community Earth System Model with both uniform (110 km) resolution and the variable-resolution configuration (VR-CESM-SONT, from 110 to 7 km) to study the effect of refined spatial resolution on projections of extreme strong and weak wind speeds in the Great Lakes region under end-of-century RCP8.5 forcing. The variable-resolution configuration projects strengthening of strong-wind events in the refined region with the opposite occurring in the uniform-resolution simulation. The two configurations provide consistent changes to synoptic-scale circulations associated with high-wind events. However, only the variable-resolution configuration projects weaker static stability, enhanced turbulent vertical mixing, and consequentially enhanced surface wind speeds because boundary layer dynamics are better captured in the refined region. Both models project increased frequency of extreme weak winds, though only VR-CESM-SONT resolves the cold-season inversions and summertime high temperatures associated with stagnant wind events. The identifiable mechanism of the changes to strong winds in VR-CESM-SONT provides confidence in its projections and demonstrates the value of enhanced spatial resolution for the study of extreme winds under climate change. Significance Statement In this study, we compare climate change projections of high and low extreme wind speeds in the Great Lakes region between a standard coarse-resolution simulation and a high-resolution simulation performed using the same climate model. The fine-resolution simulation projects strengthening high wind speeds, opposite to the coarse-resolution simulation. Both project increasing frequency of extreme weak winds, but the human-health-related impacts of stagnant winds are only captured at fine resolution. The changes in the coarse-resolution simulation are explained by changes to large-scale circulation, while the fine-resolution changes are linked to local processes the coarse model does not resolve. This helps explain the diverging projections of strong winds and gives greater credibility to the fine-resolution simulation.

The effect of explicit convection on simulated malaria transmission across Africa.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Weaker land–atmosphere coupling in global storm-resolving simulation

The debate on the sign of the soil moisture-precipitation feedback remains open. On the one hand, studies using global coarse-resolution climate models have found strong positive feedback. However, such models cannot represent convection explicitly. On the other hand, studies using km-scale regional climate models and explicit convection have reported negative feedback. Yet, the large-scale circulation is prescribed in such models. This study revisits the soil moisture-precipitation feedback using global, coupled simulations conducted for 1 y with explicit convection and compares the results to coarse-resolution simulations with parameterized convection. We find significant differences in a majority of points with feedback that is weaker and dominantly negative with explicit convection. The model with explicit convection is more often in a wet regime and prefers the triggering of convection over dry soil in the presence of soil moisture heterogeneity, in contrast to the coarse-resolution model. Further analysis indicates that the feedback not only between soil moisture and evapotranspiration but also between evapotranspiration and precipitation is weaker, in better agreement with observations. Our findings suggest that coarse-resolution models may not be well suited to study aspects of climate change over land such as changes in droughts and heatwaves.

Estimates of Southern Hemispheric Gravity Wave Momentum Fluxes across Observations, Reanalyses, and Kilometer-Scale Numerical Weather Prediction Model

Abstract Gravity waves (GWs) are among the key drivers of the meridional overturning circulation in the mesosphere and upper stratosphere. Their representation in climate models suffers from insufficient resolution and limited observational constraints on their parameterizations. This obscures assessments of middle atmospheric circulation changes in a changing climate. This study presents a comprehensive analysis of stratospheric GW activity above and downstream of the Andes from 1 to 15 August 2019, with special focus on GW representation ranging from an unprecedented kilometer-scale global forecast model (1.4 km ECMWF IFS), ground-based Rayleigh lidar (CORAL) observations, modern reanalysis (ERA5), to a coarse-resolution climate model (EMAC). Resolved vertical flux of zonal GW momentum (GWMF) is found to be stronger by a factor of at least 2–2.5 in IFS compared to ERA5. Compared to resolved GWMF in IFS, parameterizations in ERA5 and EMAC continue to inaccurately generate excessive GWMF poleward of 60°S, yielding prominent differences between resolved and parameterized GWMFs. A like-to-like validation of GW profiles in IFS and ERA5 reveals similar wave structures. Still, even at ∼1 km resolution, the resolved waves in IFS are weaker than those observed by lidar. Further, GWMF estimates across datasets reveal that temperature-based proxies, based on midfrequency approximations for linear GWs, overestimate GWMF due to simplifications and uncertainties in GW wavelength estimation from data. Overall, the analysis provides GWMF benchmarks for parameterization validation and calls for three-dimensional GW parameterizations, better upper-boundary treatment, and vertical resolution increases commensurate with increases in horizontal resolution in models, for a more realistic GW analysis. Significance Statement Gravity wave–induced momentum forcing forms a key component of the middle atmospheric circulation. However, complete knowledge of gravity waves, their atmospheric effects, and their long-term trends are obscured due to limited global observations, and the inability of current climate models to fully resolve them. This study combines a kilometer-scale forecast model, modern reanalysis, and a coarse-resolution climate model to first compare the resolved and parameterized momentum fluxes by gravity waves generated over the Andes, and then evaluate the fluxes using a state-of-the-art ground-based Rayleigh lidar. Our analysis reveals shortcomings in current model parameterizations of gravity waves in the middle atmosphere and highlights the sensitivity of the estimated flux to the formulation used.

On the Contribution of Transient Diabatic Processes to Ocean Heat Transport and Temperature Variability

Abstract Time-varying processes contribute to ocean heat transport and are important to understand for accurate climate modeling. While past studies have quantified time-varying contributions to advective transport, less attention has been given to diabatic processes such as surface forcing and mixing. Using a global eddy-permitting ocean model we quantify the contribution of time-variable processes to meridional and diathermal (warm to cold) heat transport at different time scales using a temporal eddy-mean decomposition performed in the temperature–latitude plane. Time-varying contributions to meridional heat transport occur predominantly at mesoscale eddy-dominated midlatitudes and in the tropics, associated with the seasonal cycle and tropical instability waves. The seasonal cycle is a dominant driver of surface flux– and mixing-driven diathermal heat transports. Nonseasonal (and nondiurnal) processes contribute up to about 10% of the total. We show that transient contributions to diathermal heat transport can be interpreted as sources of Eulerian temperature variance. We thus extend recent work on the drivers of temperature variability by evaluating the role of mixing. Mixing dampens seasonal and diurnal temperature variability, except near the equator where it can be a source of seasonal variability. At mesoscale time scales mixing drives variability within and near the base of the boundary layer, the mechanisms of which are explored using a column model. We suggest that climate models that do not resolve the mesoscale may be missing the rectified heat transport associated with high-frequency diabatic processes, in addition to the adiabatic eddy fluxes that are commonly parameterized. Significance Statement Ocean heat transport plays a key role in determining how the climate responds to changes in forcing. This transport is influenced by a range of processes that vary with time. Previous research has quantified time-varying sources of lateral heat transport, such as mesoscale eddies and overturning circulation cells. However, time-varying “diabatic processes,” such as surface forcing and unresolved turbulent mixing, have received less attention. Here, we quantify these effects using a global ocean model. We find a dominant role for the seasonal cycle in driving diabatic heat transport, but processes on shorter time scales also contribute. Our results suggest that temporal variations in turbulent mixing are an important contributor to heat transport but may not be resolved in coarse-resolution climate models.

Historical and future trends in South Asian monsoon low pressure systems in a high-resolution model ensemble

Historical trends in monsoon low pressure systems (LPS), the dominant rain-bearing weather system of South Asia, have been difficult to assess due to changes in the observing network. Future projections have also remained uncertain because prior studies concluded that many coarse-resolution climate models do not accurately simulate LPS. Here, we examine changes in South Asian monsoon LPS simulated by an ensemble of global models, including some with high spatial resolution, that we show skillfully represent LPS. In the ensemble mean, the number of strong LPS (monsoon depressions) decreased over the last 65 years (1950–2014) by about 15% while no trend was detected for weaker LPS (monsoon lows). The reduction in depression counts then moderated, yielding no trend in the periods 1980–2050 or 2015–2050. The ensemble mean projects a shift in genesis from ocean to land and an increase in LPS precipitation of at least 7% K−1, which together contribute to a projected increase in seasonal mean and extreme precipitation over central India.

Temporal Variability of Global Surface Eddy Diffusivities: Estimates and Machine Learning Prediction

Abstract Mesoscale eddies, ubiquitous in the global ocean, play a key role in the climate system by stirring and mixing key tracers. Estimating, understanding, and predicting eddy diffusivity is of great significance for designing suitable eddy parameterization schemes for coarse-resolution climate models. This is because climate model results are sensitive to the choice of eddy diffusivity magnitudes. Using 24-yr satellite altimeter data and a Lagrangian approach, we estimate time-dependent global surface cross-stream eddy diffusivities. We found that eddy diffusivity has nonnegligible temporal variability, and the regionally averaged eddy diffusivity is significantly correlated with the climate indices, including the North Pacific Gyre Oscillation, Atlantic multidecadal oscillation, El Niño–Southern Oscillation, Pacific decadal oscillation, and dipole mode index. We also found that, compared to the suppressed mixing length theory, random forest (RF) is more effective in capturing the temporal variability of regionally averaged eddy diffusivity. Our results indicate the need for using time-dependent eddy mixing coefficients in climate models and demonstrate the advantage of RF in predicting mixing temporal variability. Significance Statement Mixing induced by ocean eddies can greatly modulate the ocean circulation and climate variability. Steady eddy mixing coefficients are often specified in coarse-resolution climate models. However, using satellite observations, we show that the eddy mixing rate has significant temporal variability at the global ocean surface. The regional temporal variability of eddy mixing is linked with large-scale climate variability (e.g., North Pacific Gyre Oscillation and Atlantic multidecadal oscillation). We found that random forest, a user-friendly machine learning algorithm, is a better tool to predict the mixing temporal variability than the conventional mixing theory. This study suggests the possibility of improving climate model performance by using time-dependent eddy mixing coefficients inferred from machine learning methods.

The impact of global changes in near-term climate forcers on East Africa’s climate

Climate change and air pollution are two interconnected daunting environmental challenges of the twenty-first century. Globally, stringent public health and environmental policies are set to mitigate the emissions of near-term climate forcers (NTCFs) because they double as air pollutants. While the global climate impact of NTCF mitigation has been investigated using coarse resolution climate models, the fine scale regional climate impacts over East Africa are not fully known. This study presents the first 2021–2055 downscaled model results of two future scenarios which both have increasing greenhouse gas emissions but with weak (SSP3-7.0) versus strong (SSP3-7.0_lowNTCF) levels of air quality control. NTCF mitigation is defined here as SSP3-7.0_lowNTCF–SSP3-7.0. The results reveal that NTCF mitigation could cause an increase in annual mean surface temperature ranging from 0.005 to 0.01 °C decade−1 over parts of Kenya, Ethiopia and Somalia. It could also cause an increase in annual mean precipitation ranging from 0.1 to 1 mm month−1 decade−1 over parts of Uganda, Kenya, Tanzania, South Sudan and Ethiopia. Majority of the precipitation increase is projected to occur during the MAM season. On the other hand, Zambia, Malawi and southern Tanzania could also experience a decrease in annual mean precipitation by up to 0.5 mm month−1 decade−1. Majority of this decrease is projected to occur during the DJF season. These findings suggest that pursuing NTCF mitigation alone while ignoring greenhouse gas emissions will cause additional climate change over East Africa. Mitigating both of them concurrently would be a better policy option.

Impacts of Climate Change on Extreme Climate Indices in Türkiye Driven by High-Resolution Downscaled CMIP6 Climate Models

In this study, the latest release of all available Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models with two future scenarios of Shared Socio-Economic Pathways, SSP2-4.5 and SSP5-8.5, over the period 2015–2100 are utilized in diagnosing climate extremes in Türkiye. Coarse-resolution climate models were downscaled to a 0.1° × 0.1° (~9 km) spatial resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) dataset based on three types of quantile mapping: quantile mapping, detrended quantile mapping, and quantile delta mapping. The temporal variations of the 12 extreme precipitation indices (EPIs) and 12 extreme temperature indices (ETIs) from 2015 to 2100 consistently suggest drier conditions, in addition to more frequent and severe precipitation extremes and warming temperature extremes in Türkiye, under the two future scenarios. The SSP5-8.5 scenario indicates more severe water stress than the SSP2-4.5 scenario; the total precipitation decreases up to 20% for Aegean and Mediterranean regions of Türkiye. Precipitation extremes indicate a decrease in the frequency of heavy rains but an increase in very heavy rains and also an increasing amount of the total precipitation from very heavy rain days. Temperature extremes such as the coldest, warmest, and mean daily maximum temperature are expected to increase across all regions of Türkiye, indicating warming conditions by up to 7.5 °C by the end of the century. Additionally, the coldest daily maximums also exhibit higher variability to climate change in the subregions Aegean, Southeastern Anatolia, Marmara, and Mediterranean regions of Türkiye while the mean daily maximum temperature showed greater sensitivity in the Black Sea, Central Anatolia, and Eastern Anatolia regions.

Projected increase in windstorm severity and contribution from sting jets over the UK and Ireland

Extratropical windstorms can lead to extreme impacts in the UK; however, future changes in windstorm frequency and intensity are uncertain due to natural variability and lack of model consensus. Further uncertainty arises due to unresolved or poorly represented processes which can reduce the validity of traditional coarse resolution climate models. This study demonstrates the added value of higher resolution simulations in their representation of extreme windstorms. It also assesses future changes in windstorms in the first ensemble of convection-permitting model (CPM, 2.2 km grid spacing) projections over the UK, released as part of the UKCP18 project, and compares these to changes in the driving 60 km global climate model (GCM) and 12 km regional climate model (RCM). The representation of wind gusts in the CPM and RCM is improved with respect to ERA-Interim reanalysis data compared to observations. Both models are largely similar except over areas with orographic influences where the CPM provides greater detail and simulates higher wind gusts. In contrast, the GCM simulates lower wind gusts and underestimates windstorm intensity; the improvements from the RCM and CPM are partially linked to better representation of cold conveyor belts and sting jets at km-scale. The CPM projects an increase in the frequency of extreme windstorms. In line with a previous study, a large contribution comes from storms that develop sting jets which now seems to be a robust response of windstorms to a warming climate. Similar projected increases are also found in the coarser resolution models, although differences in projections exist at a regional scale in the CPM compared to the RCM and GCM. Overall, higher resolution simulations improve the representation of windstorms, offering greater detail at the local scale which may provide better information for impact modelling, though large-scale projected changes in the assessed storm severity index are insensitive to the model resolutions in the simulations tested here.

Capturing and Attributing the Rainfall Regime Intensification in the West African Sahel with CMIP6 Models

Abstract Rainfall in the Sahel is extremely variable on daily to multidecadal time scales, challenging climate models to realistically simulate its past and future evolution and questioning their relevance for defining suitable climate change adaptation strategies. Improving confidence in climate models may be achieved by (i) evaluating their capacity for reproducing observed climatic evolution and (ii) attributing these evolutions. Moreover, there is a need to consider relevant climatic indicators, from an end-user point of view. Fully coupled (CMIP6-AOGCM) models with idealized detection and attribution forcings (DAMIP) as well as atmosphere-only simulations (AMIP) are used to investigate the respective roles of external forcing factors and internal climate variability in the observed intensification of the Sahelian rainfall regime. We show that CMIP6 models contain signs of the intensification of the rainfall regime as detected over the past 35 years from a regional daily observations network. Both the increase in intensity and occurrence of wet days, as well as that of extreme daily rainfall, are remarkably well reproduced by historical simulations incorporating anthropogenic forcing factors, with anthropogenic aerosols contributing the largest share of this trend. Though more strongly affected by model structure uncertainty, the greenhouse gas forcing also displays noticeably robust features. Models are shown to fail at simulating the observed dry extreme evolution. These findings give incentive for further investigating the underlying physical mechanisms that drive the Sahelian rainfall regime evolution at regional to subregional scales. Furthermore, future hydroclimatic trajectories in the Sahel should be explored, though particular caution is required as to which rainfall indicator to consider. Significance Statement The rainfall regime at a particular location is crucial to human and ecosystem livelihoods. Changes in rainfall regime characteristics on multidecadal time scales result from both the effects of external forcing factors on the climate and of its internal variability, with this latter aspect becoming more prominent on small spatial scales. In this study, several state-of-the-art climate simulations are used to document the rainfall regime evolution of the past 65 years in the Sahel, in terms of amplitude, timing, and causes. It is shown that large-scale anthropogenic factors have a substantial imprint, modulated to some extent by internal variability. These findings demonstrate that coarse-resolution climate models are a well-suited tool to investigate the recent intensification of rainfall in the Sahel, and may provide valuable information for climate change adaptation planning.

Future projection of the African easterly waves in a high-resolution atmospheric general circulation model

Simulating the features of the African Easterly Waves (AEWs), such as their westward propagation off the east Atlantic coast, is challenging for coarse-resolution climate models. In this study, we use High-Resolution Atmospheric Model (HiRAM) to simulate AEWs and analyze their future projections by the end of the twenty first century. The simulations are performed globally at a horizontal resolution of sim 25 km. The model uses shallow convective parameterization for moist convection and stratiform cloudiness. Future projections are conducted using representative concentration pathway 8.5. The AEWs are separated with respect to their periods as 3–5- and 6–9-day period AEWs, and bandpass filtering is used to filter the waves from the mean flow. HiRAM simulates structure and propagation of the waves well; however, it tends to overestimate the associated precipitation. In the future, the AEW precipitation and intensity of the circulation will considerably increase. The northward extent of the AEW track also shows a significant increase in the future. Enhanced baroclinic overturning and eddy available potential energy generated due to diabatic heating is also observed in the future.

Modifying the Mixed Layer Eddy Parameterization to Include Frontogenesis Arrest by Boundary Layer Turbulence

Abstract Current submesoscale restratification parameterizations, which help set mixed layer depth in global climate models, depend on a simplistic scaling of frontal width shown to be unreliable in several circumstances. Observations and theory indicate that frontogenesis is common, but stable frontal widths arise in the presence of turbulence and instabilities that participate in keeping fronts at the scale observed, the arrested scale. Here we propose a new scaling law for arrested frontal width as a function of turbulent fluxes via the turbulent thermal wind (TTW) balance. A variety of large-eddy simulations (LES) of strain-induced fronts and TTW-induced filaments are used to evaluate this scaling. Frontal width given by boundary layer parameters drawn from observations in the General Ocean Turbulence Model (GOTM) are found qualitatively consistent with the observed range in regions of active submesoscales. The new arrested front scaling is used to modify the mixed layer eddy restratification parameterization commonly used in coarse-resolution climate models. Results in CESM-POP2 reveal the climate model’s sensitivity to the parameterization update and changes in model biases. A comprehensive multimodel study is in planning for further testing. Significance Statement The ocean surface plays a major role in the climate system, primarily through exchange in properties, such as in heat and carbon, between the ocean and atmosphere. Accurate model representation of ocean surface processes is crucial for climate simulations, yet they tend to be too small, fast, or complex to be resolved. Significant efforts lie in approximating these small-scale processes using reduced expressions that are solved by the model. This study presents an improved representation of the ocean surface in climate models by capturing some of the synergy that has been missing between the processes that define it. Results encourage further testing across a wider range of models to comprehensively evaluate the effects of this adjustment in climate simulations.

Changes in freezing rain occurrence over eastern Canada using convection-permitting climate simulations

Freezing precipitation has major consequences for ground and air transportation, the health of citizens, and power networks. Previous studies using coarse resolution climate models have shown a northward migration of freezing rain in the future. Increased model resolution can better define local topography leading to improved representation of conditions that are favorable for freezing rain. The goal of this study is to examine the climatology and characteristics of future freezing rain events using very-high resolution climate simulations. Historical and pseudo-global warming simulations with a 4-km horizontal grid length were used and compared with available observations. Simulations revealed a northerly shift of freezing rain occurrence, and an increase in the winter. Freezing rain was still shown to occur in the Saint-Lawrence River Valley in a warmer climate, primarily due to stronger wind channeling. Up to 50% of the future freezing rain events also occurred in present day climate within 12 h of each other. In northern Maine, they are typically shorter than 6 h in current climate and longer than 6 h in warmer conditions due to the onset of precipitation during low-pressure systems occurrences. The occurrence of freezing rain also locally increases slightly north of Québec City in a warmer climate because of freezing rain that is produced by warm rain processes. Overall, the study shows that high-resolution regional climate simulations are needed to study freezing rain events in warmer climate conditions, because high horizontal resolutions better define small-scale topographic features and local physical mechanisms that have an influence on these events.

A physical–statistical recipe for representation of small-scale oceanic turbulent mixing in climate models

It is well established that small-scale cross-density (diapycnal) turbulent mixing induced by breaking of overturns in the interior of the ocean plays a significant role in sustaining the deep ocean circulation and in regulating tracer budgets such as those of heat, carbon and nutrients. There has been significant progress in the fluid mechanical understanding of the physics of breaking internal waves. Connection of the microphysics of such turbulence to the larger scale dynamics, however, is significantly underdeveloped. We offer a hybrid theoretical–statistical approach, informed by observations, to make such a link. By doing so, we define a bulk flux coefficient, $\varGamma _B$ , which represents the partitioning of energy available to an ‘ocean box’ (such as a grid cell of a coarse resolution climate model), from winds, tides, and other sources, into mixing and dissipation. Here, $\varGamma _B$ depends on both the statistical distribution of turbulent patches and the flux coefficient associated with individual patches, $\varGamma _i$ . We rely on recent parametrizations of $\varGamma _i$ and the seeming universal characteristics of statistics of turbulent patches to infer $\varGamma _B$ , which is the essential quantity for representation of turbulent diffusivity in climate models. By applying our approach to climatology and global tidal estimates, we show that, on a basin scale, energetic mixing zones exhibit moderately efficient mixing that induces significant vertical density fluxes, while quiet zones (with small background turbulence levels), although highly efficient in mixing, exhibit minimal vertical fluxes. The transition between the less energetic to more energetic zones marks regions of intense upwelling and downwelling of deep waters. We suggest that such upwelling and downwelling may be stronger than previously estimated, which in turn has direct implications for the closure of the deep branch of the ocean meridional overturning circulation as well as for the associated tracer budgets.

Towards an improved treatment of cloud–radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4

Abstract. The representation of unresolved clouds in radiation schemes of coarse-resolution weather and climate models has progressed noticeably over the past years. Nevertheless, a lot of room remains for improvement, as the current picture is by no means complete. The main objective of the present study is to advance the cloud–radiation interaction parameterization, focusing on the issues related to model misrepresentation of cloud horizontal inhomogeneity. This subject is addressed with the Tripleclouds radiative solver, the fundamental feature of which is the inclusion of the optically thicker and thinner cloud fraction. The research challenge is to optimally set the pair of cloud condensates characterizing the two cloudy regions and the corresponding geometrical split of layer cloudiness. A diverse cloud field data set was collected for the analysis, comprising case studies of stratocumulus, cirrus and cumulonimbus. The primary goal is to assess the validity of the global cloud variability estimate along with various condensate distribution assumptions. More sophisticated parameterizations are subsequently explored, optimizing the treatment of overcast as well as extremely heterogeneous cloudiness. The radiative diagnostics including atmospheric heating rate and net surface flux are consistently studied using the Tripleclouds method, evaluated against a three-dimensional radiation computation. The performance of Tripleclouds mostly significantly surpasses the calculation on horizontally homogeneous cloudiness. The effect of horizontal photon transport is further quantified. The overall conclusions are intrinsically different for each particular cloud type, encouraging endeavors to enhance the use of cloud-regime-dependent methodologies in next-generation atmospheric models. This study, highlighting the Tripleclouds potential for three essential cloud types, signifies the need for more research examining a broader spectrum of cloud morphologies.

Projected Changes to Australian Marine Heatwaves

AbstractMarine heatwaves are extreme oceanic events that can have severe impacts on ecosystems and economies. Recent studies have suggested future increases in the severity and duration of these events. However, these findings are based on coarse resolution climate models that cannot resolve important ocean structures. This study uses a 0.25° coupled regional model to quantify how marine heatwaves around Australia respond under a high emissions scenario. We find that increases to future marine heatwave statistics are primarily caused by changes in the mean temperature, with changes in variability important in some areas. Temperature variance changes are most prominent close to Tasmania, with a covariance temperature budget demonstrating that this is linked to large simulated increases in East Australian Current transport and variability. Close to New Zealand, changes in surface heat fluxes also play an important role. These results quantify the response of marine heatwave characteristics to future temperature changes.

Elevated temperatures increase growth and enhance foraging performances of a marine gastropod

The oceans continue to warm due to rising atmospheric greenhouse gas concentrations. Most climate-change studies of aquaculture species use temperature changes based on coarse-resolution climate models and without considering thermal ranges of an animal. Coarse-resolution climate models are generated by global-scale data, which is insufficient to capture the conditions of coastal areas where most aquaculture activity occurs. Therefore, ocean warming research on coastal organisms requires a more comprehensive design to include broad temperature gradients. By using the ecologically and commercially important coastal whelk Rapana venosa, we combined long-term and short-term experiments and selected 4 temperature treatments (19, 23, 27, and 30°C) to simulate different scenarios to test ocean warming effects on growth rates and foraging performances of whelks. We found that elevated temperature within the whelk’s thermal range (23 and 27°C) significantly increased growth rates and enhanced foraging performances of marine whelks when compared to the current temperature (19°C). Conversely, the whelk’s performance collapsed at 30°C in terms of both growth and foraging behavior. Our research clearly shows that local conditions and the tolerance range of a species must be considered to develop meaningful information for testing the effects of a changing climate. Our study suggests that rapa whelks may increase their feeding and reach larger sizes during warmer periods. Moreover, our study may provide a foundation for future climate research on aquaculture species.

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

AbstractNeural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a superparameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s−1. In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

Greater Future U.K. Winter Precipitation Increase in New Convection-Permitting Scenarios

AbstractFor the first time, a model at a resolution on par with operational weather forecast models has been used for national climate scenarios. An ensemble of 12 climate change projections at convection-permitting (2.2 km) scale has been run for the United Kingdom, as part of the UK Climate Projections (UKCP) project. Contrary to previous studies, these show greater future increases in winter mean precipitation in the convection-permitting model compared with the coarser (12 km) driving model. A large part (60%) of the future increase in winter precipitation occurrence over land comes from an increase in convective showers in the 2.2 km model, which are most likely triggered over the sea and advected inland with potentially further development. In the 12 km model, increases in precipitation occurrence over the sea, largely due to an increase in convective showers, do not extend over the land. This is partly due to known limitations of the convection parameterization scheme, used in conventional coarse-resolution climate models, which acts locally without direct memory and so has no ability to advect diagnosed convection over the land or trigger new showers along convective outflow boundaries. This study shows that the importance of accurately representing convection extends beyond short-duration precipitation extremes and the summer season to projecting future changes in mean precipitation in winter.