Elevation-dependent warming in global climate model simulations at high spatial resolution

Under the Coordinated Regional Climate Downscaling Experiments‐East Asia (CORDEX‐EA‐II), the outputs from two regional climate models (RCMs) driven by four global climate models (GCMs) are used to investigate the characteristics and possible mechanisms of the projected elevation‐dependent warming (EDW) over the Tibetan Plateau (TP) under the Representative Concentration Pathway emission scenario 8.5 (RCP8.5). Results show that widespread warming over the TP is projected with considerable disagreements in warming intensity and the maximum warming center among RCMs. The largest spread in the surface air temperature (Tas) projections is found above 5,000 m, indicating that a large uncertainty exists over the higher elevations. A marked EDW signal over the TP is simulated under the RCP 8.5 by the multi‐RCM ensemble mean for all seasons, particularly in autumn. Based on the analysis of the surface energy budget, it is found that the surface albedo feedback (SAF) is the primary contributor to EDW and acts as the main source of uncertainty in EDW projections among RCMs. The downward longwave radiation (DLW) is found to be the dominant factor in regulating Tas change over the TP, and its contribution to EDW is model‐dependent. Furthermore, the structure and magnitude of projected EDW are sensitive to the RCM physics and driving GCM, as they can alter the projections of snow cover and albedo, which modulate the simulated SAF and its effect on EDW. Additionally, RegCM4 shows a higher sensitivity to the anthropogenic greenhouse forcing than WRF, evidenced by the larger temperature projections and stronger EDW signal in RegCM4.

High-altitude regions have been identified as hotspots of climate change. In particular, the dependence of warming rates on elevation, known as Elevation-Dependent Warming (EDW), has been extensively discussed in the literature. Recently, the focus has expanded to the broader concept of Elevation-Dependent Climate Change (EDCC), with attention to precipitation and its extremes, given their importance for mountain hydrological resources and their role in triggering geo-hydrological hazards. Recent studies have investigated the elevational stratification of precipitation in in-situ observations and reanalysis datasets, showing a lack of uniform patterns of EDCC across the world, which point to the need for common methodologies and insight in the driving mechanisms. In this study, we extend results we obtained with the ERA5 reanalysis to CMIP6 global climate models, and study EDCC in key mountain regions of the world: Tibetan Plateau, the US Rocky Mountains, the Greater Alpine Region, and the Andes. We focus on precipitation and its extremes, assessing the ability of the models to reproduce historical patterns of stratification by comparison with ERA5 reanalysis data and other observation-based gridded datasets. We also explore how the stratification in other key climate variables, such as cloud cover, humidity, besides temperature, influence the elevational patterns of precipitation and precipitation extremes and their trends. Our analysis aims to determine whether the observed elevation-dependent precipitation patterns are primarily driven by dynamical, thermodynamical, or microphysical processes, identifying seasonal variations and the specific precipitation type (i.e., stratiform vs convective) mostly affected. Particular attention is given to the role of the model spatial resolution, including regional climate models in a case study analysis over the Greater Alpine Region.

To explore the driving mechanisms of elevation-dependent warming (EDW) over the Tibetan Plateau (TP), the output from a suite of numerical experiments with different cumulus parameterization schemes (CPs) under the Coordinated Regional Climate Downscaling Experiments-East Asia (CORDEX-EA-II) project is examined. Results show that all experiments can broadly capture the observed temperature distributions over the TP with consistent cold biases, and the spread in temperature simulations commonly increases with elevation with the maximum located around 4000–5000 m. Such disagreements among the temperature simulations could to a large extent be explained by their spreads in the surface albedo feedback (SAF). All the experiments reproduce the observed EDW below 5000 m in winter but fail to capture the observed EDW above 4500 m in spring. Further analysis suggests that the simulated EDW during winter is mainly caused by the SAF, and the clear-sky downward longwave radiation (LWclr) plays a secondary role in shaping EDW. The models’ inability in simulating EDW during spring is closely related to the SAF and the surface cloud radiative forcing (CRFs). Furthermore, the magnitude and structure of the simulated EDW are sensitive to the choice of CPs. Different CPs generate diverse snow cover fractions, which can modulate the simulated SAF and its effect on EDW. Also, the CPs show great influence on the LWclr via altering the low-level air temperature. Additionally, the mechanism for different temperature changes among the experiments varies with altitudes during summer and autumn, as the diverse temperature changes appear to be caused by the LWclr for the low altitudes while by the SAF for the middle-high altitudes.

A majority of studies suggest that elevation dependent warming (EDW) has been verified in mountainous areas. However, there is some controversy about the EDW of high mountain Asia (HMA). Based on the analysis of the data from 128 meteorological stations in the entire region for 1961–2017, we found that there was no EDW in HMA on the time scale of 1961–2017 and the spatial scale of the altitude of 3500–5000 m. The EDW in HMA is the most obvious during the period of 1998–2012. In general, after 1980, there was EDW in the altitude of 2500–5000 m. The Southeastern Tibetan Plateau always has EDW phenomenon for most of the time scales while other areas only have EDW at certain periods. Therefore, we consider that the rate of warming is higher only in specific mountain areas and time scales.

Rethinking spatiotemporal variations in air temperature over the Qilian Mountains, Western China, from 1979 to 2018

Elevation-dependent temperature change is a phenomenon found in mountain regions with complex terrain, mostly in the Himalayas and the high terrain of the Tibetan Plateau, where regions in high elevation feature high rates of warming than the region in lower elevation. This pattern referred to as elevation-dependent warming. However, does elevation-dependent warming exist in Eocene hothouse without Tibetan Plateau as well and lower altitude mountain ranges?The Eocene era is considered a replication of the future climate with high atmospheric carbon dioxide. We utilized CESM1.2 as part of the DeepMIP simulations to analyze elevation-dependent temperature change in different mountain ranges in the Eocene and explained the findings using a linear surface energy balance decomposition. The results feature a land-sea contrast with amplification over land and elevation-dependent temperature changes in all mountain ranges with distinct seasonality and pattern. The results suggest that radiative feedback processes have a strong contribution to elevation-dependent warming in the warming climate. Our modeling results provide relevant information for mountain climate change in a past hot climate. Further, the analysis opens new mystery and perspectives related to elevation-dependent warming. Keywords: Eocene, paleoclimate modeling, elevation-dependent warming, CO2 Related Publication:Kad P, Blau M T, Ha K-J, and Zhu J (2022) Elevation-dependent temperature response in early Eocene using paleoclimate model experiment. Environmental Research Letters, 17(11), 114038. (https://doi.org/10.1088/1748-9326/ac9c74)

Rapid elevation-dependent warming (EDW) in high mountain environments can accelerate the rate of change in permafrost and further induce the degradation of alpine tundra systems. However, how changes in area of alpine tundra response to EDW is difficult to be directly quantified at regional scales. Here, we project future changes in areal extent of the Koppen “alpine tundra” climate type in the Tibetan Plateau (TP), which is a potential indicator describing the climate conditions for the development of alpine tundra system, based on a suite of latest Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models under two emission scenarios (representative concentration pathways: RCP 4.5 and RCP 8.5). The results indicated that the Koppen “alpine tundra” climate type would gradually decrease in the TP for three future periods: 2011–2040, 2041–2070, and 2071–2100, especially under the RCP8.5 scenario. The decline in area of Koppen “alpine tundra” climate type mainly occurs above 2500 m above sea level (ASL) due to the EDW (> 10 °C) in the warmest months. The projected changes in area of “alpine tundra” climate type are overall consistent among different climate models, emission scenarios, and post-processing approaches (bias correction or not) used. The results obtained would thus deepen our understanding of future elevation-dependent warming and its potential influences on alpine tundra ecosystems in the Tibetan Plateau.

With current climate change trends, high elevation areas are being impacted by elevation dependent warming at increasing rates. Differential warming in mountain ecosystems has impacted biodiversity, animal behaviors and migrational patterns, fundamentally changing high elevation ecosystems and their ecosystem services. Due to difficulties accessing mountain ecosystems, research has been limited throughout the world, indicating the need for further investigation. We use data and findings from the Intergovernmental Panel on Climate Change, United Nations and other independent organizations to examine the changing functions of mountain ecosystems due to elevation dependent warming through the lens of UN Sustainable Development goal 15.4. SDG Theme: SDG 15- Life on land Type: Short talk (e.g. PowerPoint, Google Slides)

Observed changes in the HadCRUH global land surface specific humidity and CRUTEM3surface temperature from 1973 to 1999 are compared to CMIP3 archive climate modelsimulations with 20th Century forcings. Observed humidity increases are proportionatelylargest in the Northern Hemisphere, especially in winter. At the largest spatio-temporal scalesmoistening is close to the Clausius–Clapeyron scaling of the saturated specific humidity (∼7% K − 1). At smaller scales in water-limited regions, changes in specific humidity arestrongly inversely correlated with total changes in temperature. Conversely, in someregions increases are faster than implied by the Clausius–Clapeyron relation.The range of climate model specific humidity seasonal climatology and varianceencompasses the observations. The models also reproduce the magnitude of observedinterannual variance over all large regions. Observed and modelled trends andtemperature–humidity relationships are comparable except for the extratropicalSouthern Hemisphere where observations exhibit no trend but models exhibitmoistening. This may arise from: long-term biases remaining in the observations; therelative paucity of observational coverage; or common model errors. The overalldegree of consistency of anthropogenically forced models with the observationsis further evidence for anthropogenic influence on the climate of the late 20thcentury.

ABSTRACTWhile global climate models (GCMs) are useful for simulating climatic responses to perturbations in the Earth's climate system, there are many instances where higher spatial resolution information is necessary. In all instances, interpretation of interpolated or downscaled GCMs must be done cautiously because each method has its own set of assumptions and potential disadvantages. Here, we present an update to the Global Climate Data (GCD) package, which enables the package to efficiently bias correct and interpolate precipitation and air temperature output from GCM simulations to very high spatial resolutions using the delta change method. While the delta change method is relatively simple, it has previously been shown to enhance the physical representation of interpolated climate time‐series compared to directly interpolating the gridded climate time‐series to a higher spatial resolution. The bias correction methods programmed into the GCD package are univariate empirical quantile mapping (QM) and bivariate empirical joint bias correction (JBC). The skill of QM and JBC for improving GCM simulations processed with the delta change method is evaluated through comparing the cumulative distribution functions (CDFs) of the interpolated GCM simulations to the CDFs of Global Historical Climatology Network (GHCN) station observations for three test regions: Oregon (in the USA), the Alps (spanning several countries in Europe), and the Ganges Delta (in India and Bangladesh). We also assess the representation of precipitation and mean temperature joint probability distributions relative to those present in GHCN station observations. Overall, GCM simulations that are bias corrected with QM prior to being input to the delta change method perform best under our analysis.

A compositing scheme that predicts changes in tropical precipitation under climate change from changes in near-surface relative humidity (RH) and temperature is presented. As shown by earlier work, regions of high tropical precipitation in general circulation models (GCMs) are associated with high near-surface RH and temperature. Under climate change, it is found that high precipitation continues to be associated with the highest surface RH and temperatures in most CMIP5 GCMs, meaning that it is the “rank” of a given GCM grid box with respect to others that determines how much precipitation falls rather than the absolute value of surface temperature or RH change, consistent with the weak temperature gradient approximation. Further, it is demonstrated that the majority of CMIP5 GCMs are close to a threshold near which reductions in land RH produce large reductions in the RH ranking of some land regions, causing reductions in precipitation over land, particularly South America, and compensating increases over ocean. Recent work on predicting future changes in specific humidity allows the prediction of the qualitative sense of precipitation change in some GCMs when land surface humidity changes are unknown. However, the magnitudes of predicted changes are too small. Further study, perhaps into the role of radiative and land–atmosphere feedbacks, is necessary.

We assessed maximum (Tmax) and minimum (Tmin) temperatures over Pakistan’s Indus basin during the 21st century using statistical downscaling. A particular focus was given to spatiotemporal heterogeneity, reference and General Circulation Model (GCM) uncertainties, and statistical skills of regression models using an observational profile that could significantly be improved by recent high-altitude observatories. First, we characterized the basin into homogeneous climate regions using K-means clustering. Predictors from ERA-Interim reanalysis were then used to model observed temperatures skillfully and quantify reference and GCM uncertainties. Thermodynamical (dynamical) variables mainly governed reference (GCM) uncertainties. The GCM predictors under RCP4.5 and RCP8.5 scenarios were used as “new” predictors in statistical models to project ensemble temperature changes. Our analysis projected non-uniform warming but could not validate elevation-dependent warming (EDW) at the basin scale. We obtained more significant warming during the westerly-dominated seasons, with maximum heating during the winter season through Tmin changes. The most striking feature is a low-warming monsoon (with the possibility of no change to slight cooling) over the Upper Indus Basin (UIB). Therefore, the likelihood of continuing the anomalous UIB behavior during the primary melt season may not entirely be ruled out at the end of the 21st century under RCP8.5.

  High-altitude regions are argued to react more strongly to global warming compared to low-altitude regions. However, due to a combination of feedback mechanisms, micro-climatic trends, and lack of long-term observational data, elevation-dependent warming has been difficult to understand and quantify. We address this question using a surface energy balance approach where 2m air temperature variations along altitudes are quantified following changes in surface radiation and turbulent fluxes.  The turbulent fluxes in the energy balance are constrained using the thermodynamic limit of maximum power. The downwelling longwave radiation is parameterized using the semi-emperical equation by Brutsaert (1975). We used BSRN (Baseline Surface Radiation Network) and FLUXNET dataset to test our approach and found that daily variations in 2m air temperatures reasonably well (with R2 value of 0.75) along the altitude gradient. We find that for high altitudes, the downwelling longwave radiation is lesser compared to stations at low altitudes at similar latitudes for both all sky conditions and clear sky conditions. We attribute it to less absorptive mass above the high altitudinal setting, leading to lower atmospheric emissivity and changes in lower atmospheric heat storage. On the other hand, absorbed solar radiation when normalized by potential solar radiation, shows strong seasonality, which is influenced by albedo changes and water vapor content in the atmosphere.  Future work entails extending this framework to get a physically based estimate of elevation-dependent warming using the sensitivity of temperature to components in the energy balance. This understanding is crucial for anticipating the impacts of warming on water resources and ecosystems in these regions and, consequently, for developing effective adaptation and mitigation strategies.  

A recently developed horizontal eddy diffusion parameterization based on closure theory has been implemented in the CSIRO atmospheric global climate model (GCM). Detailed studies of the effects of the parameterization on the simulated atmospheric circulation, transient kinetic energy and kinetic energy spectra have been carried out for different months throughout the year. These diagnostics have been compared with corresponding results from observations and from control simulations using an ad hoc diffusion parameterization also employed in earlier works with this model. The new diffusion parameterization has improved the simulated atmospheric circulation in the following ways. Zonal and total wavenumber spectra now have approximate -3 power laws for ranges of intermediate wavenumbers, as in the observations, while the control simulation spectra are considerably flatter. Peak values of transient kinetic energy near the tropospheric jet cores are considerably larger than in the control and in better agreement with observations, particularly in boreal winter. The strength and location of the tropospheric jets are also improved with the new diffusion parameterization, with monthly averaged zonal mean winds in the Northern Hemisphere and Southern Hemisphere jet cores stronger than for the control by as much as 5 m s-1 in boreal winter and spring. The GCM simulations are also stable with longer timesteps with the new eddy diffusion parameterization. It is suggested that further improvements in GCM simulations may be achievable through parameterizations of the nondiagonal elements of the covariance matrix of eddy fluxes such as the eddy-topographic force.

Global climate models (GCMs) represent invaluable instrument for various purposes, most prominently studying climate system dynamics, evolution of past climates and climate change projections. The newest set of GCM simulations has been produced under CMIP6 initiative coordinated by the the World Climate Research Programme’s (WCRP) Working Group on Coupled Modelling. Due to their coarse spatial resolution, some kind of downscaling is necessary for applications of GCM outputs on regional and local scales, with dynamical downscaling using regional climate models (RCMs) being a common solution of this issue. Clearly, the outputs of RCMs are influenced by the boundary conditions provided at the lateral boundaries of the integration domain. The magnitude of this influence is subject of ongoing research and depends on various aspects including the geographical region, temporal scale, climatic variable etc. Nevertheless, previous studies proved that an analysis of boundary conditions is needed for for proper RCM evaluation. Especially with regard to potential error propagation.              In present study we evaluate the simulation of CNRM-ESM2-1, one of the CMIP6 GCMs, and compare it to other CMIP6 ensemble members. The CNRM-ESM2-1 is being used as driving model for convective-permitting simulation of Aladin-CLIMATE RCM within Czech national project PERUN aimed at creation of updated climate change scenarios for the Czech Republic. We concentrate on historical GCM simulations in the period of 1990-2014, and use reanalysis ERA5 as the reference. The analysis is conducted over the boundaries of Aladin-CLIMATE/CZ intergration domain, which covers Central Europe. The deviations of the driving GCM (CNRM-ESM2-1) from reanalysis are compared to other CMIP6 GCMs. We also evaluate uncertainty arising from natural climate variability using perturbed initial conditions ensemble of CNRM-ESM2-1.