Climate Downscaling Research Articles

Spatio‐temporal downscaling emulator for regional climate models

AbstractRegional climate models (RCM) describe the mesoscale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from general circulation models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of compute time more than statistical climate downscaling. In this article, we describe how to use a spatio‐temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using VC. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non‐stationary marginal effects, which means that the downscaling output can vary over space. Furthermore, the model has flexibility to estimate the mean of any variable in space and time, and has good prediction results. INLA was the fastest method for all the cases, and the approximation with best accuracy to estimate the different parameters from the model and the posterior distribution of the response variable.

Assessment of climate simulation over the Tibetan Plateau based on high-resolution multi-RCM within CORDEX-EA-II

The climate simulation over the Tibetan Plateau (TP) remains a challenge for climate models, limiting the reliability of future climate projections over there. This study focuses on the performance of five regional climate models (RCMs) under the Coordinated Regional Climate Downscaling Experiment-East Asia (CORDEX-EA-II) in reproducing the climate over the TP for the period 1981–2015. All simulations are driven by ERA-Interim reanalysis at a resolution of 25 km (0.22°), and are evaluated against the two observation datasets (CN05 and ERA5) as well as inter-compared to identify their weaknesses and differences among them. The results show that all RCMs can capture the observed spatial distribution and annual cycle of temperature and precipitation over the TP, with overall cold and wet biases. The cold biases exhibit a clear altitude dependence, while there is no obvious relationship between precipitation biases and altitude, and most of the RCMs advance the time with the maximum monthly precipitation by 1 month over the northwestern TP. The distribution of temperature trends and the signal of elevation-dependent warming in the observation are well reproduced by most of the RCMs. Inconsistent precipitation trends are found between the two observational datasets during summer and autumn, and the simulated precipitation trends are more consistent with that in ERA5 than in CN05. Further analysis suggests that the downward shortwave radiation is the main contributor to the diverse temperature simulations among the RCMs for all seasons, and the inability of RCMs to reproduce the precipitation trends may be related to the misrepresentation of convection processes. Moreover, some of the RCMs perform better than ERA5 over the TP, demonstrating the importance of dynamical downscaling over the TP.

EVALUATION OF SDSM MODELS FOR CLIMATE PREDICTIONS IN BANGLADESH

As one of the poorest countries in the world, agriculture is Bangladesh’s economic pillar, leading to Bangladesh’s economy becoming vulnerable to global warming. The generation of high-resolution climate predictions in Bangladesh can help to reduce the huge damage and losses inflicted by disasters linked to climate. The statistical downscaling model (SDSM) is the most widely used software on robust climate downscaling and prediction analysis. In this study, by using the SDSM model, we established the statistical relationship between observed climate data in Bangladesh and the large-scale low-resolution NCEP data and used three statistical indicators to evaluate the prediction performance of the SDSM software. Our results show that the SDSM software is more suitable for forecasting humidity/temperature in Bangladesh than rainfall.

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas

Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.

Impact of climate change on hydrologic components using CORDEX Africa climate model in Gilgel Gibe 1 watershed Ethiopia

This study aimed to assess the impact of climate change on the hydrological components of Gilgel Gibe-1 using the ensemble of Coordinated Regional Climate Downscaling Experiments (CORDEX) Africa Domain namely REMO2009, HIRAM5, CCLM4-8 and RCA4 Regional Climate Models (RCMs) simulations. The performance of these RCM models was evaluated using the observed data from 1985 to 2005 and the ensemble was shown to simulate rainfall and air temperature better than individual RCMs. Then the RCMs ensemble data for historical and future projections from 2026 to 2055 years under RCP4.5 and RCP8.5 were corrected for bias and used to evaluate the impact of climate change. A non-linear bias correction and the monthly mean biases corrections method is used to adjust precipitation and temperature respectively. The future projection shows that; rainfall is expected to increase from August to December with maximum values of 1.97–235.23% under RCP4.5. The maximum temperature is expected to increase with maximum value of 1.62 °C under RCP8.5 in the study area. The calibrated and validated Soil and Water Assessment Tool (SWAT) model was used to investigate the impact of climate change on hydrologic components such as surface runoff, lateral flow, water yield, evapotranspiration and sediment yield. The SWAT model was calibrated and validated using monthly stream flow with the statistical performance of R2 value of 0.82 and NSE value of 0.72 for calibration and R2 of 0.79 and NSE of 0.67 for validation. Surface runoff and sediment yield are expected to increase from August to December under RCP4.5 and from August to February under RCP8.5. Overall both surface runoff and sediment yield are expected to increase in the future.

Projected Future Changes in Extreme Climate Indices over Central Asia Using RegCM4.3.5

This work projected future extreme climate indices’ changes over Central Asia (The Coordinated Regional Climate Downscaling Experiment—CORDEX Region 8). Changes were calculated for 2071–2100 relative to 1971–2000. Climate simulations were obtained by downscaling the RegCM4.3.5 to 50 km resolution under RCP4.5 and 8.5 with HadGEM2-ES and MPI-ESM-MR. The results indicate that the Central Asian domain will experience warmer and more extreme temperatures with increasing radiative forcing. The annual lowest value of minimum daily temperature was simulated to increase remarkably, up to 8 degrees, especially in high latitudes, with a more than 12 degree increase projected over Siberia. A strong growth in the percentage of warm nights and an increase in the days of warm spells for the whole region, with a decrease in cold spell duration, are anticipated. Model results show an expected reduction of up to 30% in precipitation totals over the domain, except for the increased precipitation over Siberia, the Himalayas, and Tibetan Plateau. Extreme precipitation events are projected to have an increase of 20% over the whole domain, with an 80% increase over high topographical areas.

Analysis of synoptic weather patterns of heatwave events

Heatwaves (HWs) are expected to increase both in duration and intensity in the next decades, but little is known about their synoptic and mesoscalar behavior, which is especially important in mid-latitude regions. Most climate research has focused on temperature analysis to characterize HWs. We propose that a combination of temperature and synoptic patterns is a better way to define and understand HWs because including atmospheric circulation patterns provides information about different HW structures that can irregularly affect the territory, and illustrate this approach at the regional and urban scales using the Iberian Peninsula and the Metropolitan Area of Barcelona as case studies. We first select HW events from 1950 to 2020 and apply a multivariate analysis to identify synoptic patterns based on mean sea level pressure, geopotential height at 500 hPa, and maximum daily 2 m temperature. The results indicate that four synoptic patterns reproduce at least 50% of the variance in HWs, namely, “stationary and stable”, “dynamic and advective”, “stationary and advective”, and “dynamic, advective and undulated”. Next, we apply the analysis to the Representative Concentration Pathway future scenarios (RCPs) 4.5 and 8.5 from the Coordinated Regional Climate Downscaling Experiment (CORDEX) to determine how these synoptic trends can change in the future. The analysis shows that the four synoptic patterns continue to explain 55 to 60% of the variance in HWs. Future HW events will be characterized by an increase in geopotential height at 500 hPa due to the northward shift of the anticyclonic ridge. This is especially true for RCP8.5, which simulates business as usual incrementing fossil fuel use and additionally shows an increase in atmospheric dynamism in north advections from all directions in comparison with RCP4.5. These findings point to the importance of considering the geopotential height in HW prediction, as well as the direction of advections.

Climate downscaling for regional models with a neural network: A Hawaiian example

Global ocean/atmosphere circulation models (GCMs) allow us to better understand and predict the behavior of the ocean and atmosphere at large spatial and temporal scales; however, they may be unsuited for regional studies due to their low spatial resolution. We have developed a method for downscaling GCMs data using an artificial neural network to reconstruct high spatial resolution regional fields. We use these downscaled fields to force a regional ocean model and examine the efficacy of the method and results. The new downscaling method combines the multivariate empirical orthogonal function (EOF) with a convolutional neural network. Utilizing an EOF decomposition of high-resolution observations and/or regional model output, we train the neural network so that it will produce high-resolution fields based on the low-resolution GCM input. We apply this method to downscale GCM near-surface wind vectors in the region of the Hawaiian Islands and force a regional ocean simulation with the downscaled winds. We compare this to a simulation directly forced by the GCM winds. The results show that the ocean simulation forced by the downscaled GCM winds demonstrates a significant improvement in capturing the ocean dynamics: increasing wind energy input from the shear regions around the islands, while decreasing it in the lee of islands. In addition, the downscaled wind transfers twice as much energy to the deep ocean as the GCM wind.

Concurrent daytime and nighttime heatwaves in the late 21st century over the CORDEX‐East Asia phase 2 domain using multi‐GCM and multi‐RCM chains

AbstractThe adverse impacts of extreme heat on human health when a concurrent daytime and nighttime heatwave (CDNHW) occurs are greater than when daytime or nighttime heatwaves occur individually, because of the reduced recovery time from heat exposure. This study projects increases in CDNHW over the whole of East Asia under two Representative Concentration Pathway scenarios (RCP2.6 and RCP8.5) and two Shared Socioeconomic Pathway scenarios (SSP1‐2.6 and SSP5‐8.5). The daily maximum and minimum temperatures, which are used to define a CDNHW, are calculated of 3‐hourly temperatures of 25 km horizontal resolution produced by 12 general circulation model and regional climate model chains participating in the Coordinated Regional Climate Downscaling Experiment East Asia phase 2 project. In Historical simulation (1981–2005), occurrence period and occurrence rate of CDNHW from April to September area‐averaged in East Asia are 10.9 days and 0.9%, respectively. In projections for the future (2071–2100), occurrence period and occurrence rate of CDNHW will be 3 weeks and 3.7% (RCP2.6), 2 months and 20.5% (RCP8.5), 2 months and 15.6% (SSP1‐2.6), and 3 months and 45.7% (SSP5‐8.5). In addition, it is expected that the CDNHW intensity will increase, and the spatial extent of CDNHW will be extended. Although a CDNHW lasting less than 3 days is the most common, the proportion of CDNHWs lasting more than 10 days, compared to the total CDNHW frequency, will increase to 1.2% (RCP2.6), 7.2% (RCP8.5), 6.1% (SSP1‐2.6), and 17.3% (SSP5‐8.5) from 0.2% (Historical). Both occurrence rate and intensity of CDNHW will increase to a relatively large extent in Indochina, East and West China, and India. If the current greenhouse gas emissions continue, East Asia will experience unprecedented heat stress because the frequency and intensity of CDNHWs, which rarely occur during present‐day, will increase significantly over all regions by the end of the 21st century.

Prediction of climate change impacts on availability of surface water resources in the semi-arid Tugwi Mukosi catchment of Zimbabwe

Climate change impact drivers includes increased rainfall variability, increased extreme events, and changes in the mean behaviour of hydro-meteorological variables such as air temperature, rainfall, and runoff. As such, this study assessed the potential impacts of climate change on surface water resources in a semi-arid Tugwi Mukosi catchment of Zimbabwe. The Mann-Kendall trend analysis, climate change downscaling and streamflow modelling were used as the primary methodology. The analysis of historical hydro-climatic data trends were performed using the Mann-Kendall trend test and Sen's slope method. Equally, observed daily rainfall was extended using bias-corrected CHIRPS satellite rainfall. Thus, a total of 9 Global Climate Models including (CCCma-CanESM2, Miroc Miroc 5, MOHCHadGEM2, MPI-M-MPI-ESM-LR, and NCCNorESM1-M) on the Sveriges Meteorologiska och Hydrologiska Institut (SMHI) Rosby Centre Atmosphere model version 4 (RCA4) RCM under the Representative Concentrated Pathways 4.5 and 8.5 were extracted and analysed for the 2030s (2021–2050) and 2060s (2051–2080) period. The results show significant (p<0.05) decreasing rainfall trends at the analysed stations, as well as significant decrease in rainfall for the 2030s and the 2060s under GCMs CCCma-CanESM 2 model. The Hydrologic Engineering Centre-Hydrological modeling System (HECHMS) (1997–1999) was calibrated and validated for the period 1981–2016. Calibration results on gauged catchment runoff and peak flows are satisfactory (Ngezi observed peak discharge and simulated peak discharge at 42.4 m3/s and 34.8 m3/s respectively, NSE 62.8%, Upper Muzhwi 30 m3/s and 19.5 m3/s, NSE 53.55%, Musogwezi 18.7 m3/,s and 16.5 m3/s, NSE 58.4%). The GCM-CanESM RCP 4.5 model showed a decrease in the runoff predicted of above 17% in both the 2030s and a significant decrease in the 2060s of 28.41% and 5% respectively. The combination of climate downscaling and water resources modelling is paramount to the management of water resources in a semi-arid area. Hydrological modelling and climate downscaling techniques gives important insights for water resources planning and development to support the water sector adaptation to climate change. The methodology applies to other data-scarce and semi-arid areas whose climate change impacts require further investigation.

The contrasting role of climate variation on the population dynamics of a native and an invasive insect pest.

Since 2008, spotted-wing drosophila, Drosophila suzukii, has become a major pest of soft, thin-skinned fruits in the USA, causing significant annual yield losses. Historically, the native blueberry maggot fly, Rhagoletis mendax, has been a key blueberry pest in eastern North America and a driver of insecticide usage. After its invasion in 2011 into New Jersey (USA), D. suzukii has supplanted R. mendax as the main target of insecticide applications in the state. However, the impact of D. suzukii on the native R. mendax has not been documented, particularly in relation to local climate. Historical monitoring data from New Jersey blueberry farms were used to assess the role of climate on R. mendax and D. suzukii populations. Seasonal trap captures of R. mendax adults have decreased after D. suzukii invasion, while D. suzukii trap captures have increased. Similarly, D. suzukii first captures have occurred earlier each year, while R. mendax has been captured later in the growing season. Winter freezing and summer growing degree days were found to significantly correlate with D. suzukii activity. Using downscaled climate simulations, we projected that D. suzukii will arrive in New Jersey blueberry fields up to 5 days earlier on average by 2030 and 2 weeks earlier by 2050 with warming temperatures, exacerbating yield losses and insecticide usage. As regional temperatures are projected to warm and the invasive range continues to expand, we predict the rate of phenological development of the invasive D. suzukii and its impact on native insects to change noticeably, bringing new challenges for pest management strategies.

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.

GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE

Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially in countries with limited infrastructure and resources. However, long-term data collection is required to characterize and evaluate trends. To address these challenges, we propose a framework that uses data from the Gravity Recovery and Climate Experiment (GRACE) mission and downscaling models to generate higher-resolution (1 km) groundwater predictions. The framework is designed to be flexible, allowing users to implement any machine learning model of interest. We selected four models: deep learning model, gradient tree boosting, multi-layer perceptron, and k-nearest neighbors regressor. To evaluate the effectiveness of the framework, we offer a case study of Sunflower County, Mississippi, using well data to validate the predictions. Overall, this paper provides a valuable contribution to the field of groundwater resource management by demonstrating a framework using remote sensing data and machine learning techniques to improve monitoring and management of this critical resource, especially to those who seek a faster way to begin to use these datasets and applications.

Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin

Abstract. Bias correction (BC) is often a necessity to improve the applicability of global and regional climate model (GCM and RCM, respectively) outputs to impact assessment studies, which usually depend on multiple potentially dependent variables. To date, various BC methods have been developed which adjust climate variables separately (univariate BC) or jointly (multivariate BC) prior to their application in impact studies (i.e., the component-wise approach). Another possible approach is to first calculate the multivariate hazard index from the original, biased simulations and bias-correct the impact model output or index itself using univariate methods (direct approach). This has the advantage of circumventing the difficulties associated with correcting the inter-variable dependence of climate variables which is not considered by univariate BC methods. Using a multivariate drought index (i.e., standardized precipitation evapotranspiration index – SPEI) as an example, the present study compares different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) applied to climate model simulations stemming from different experiments at different spatial resolutions (namely Coordinated Regional Climate Downscaling Experiment (CORDEX), CORDEX Coordinated Output for Regional Evaluations (CORDEX-CORE), and 6th Coupled Intercomparison Project (CMIP6)). The BC methods are calibrated and evaluated over the same historical period (1986–2005). The proposed framework is demonstrated as a case study over a transboundary watershed, i.e., the Upper Jhelum Basin (UJB) in the Western Himalayas. Results show that (1) there is some added value of multivariate BC methods over the univariate methods in adjusting the inter-variable relationship; however, comparable performance is found for SPEI indices. (2) The best-performing BC methods exhibit a comparable performance under both approaches with a slightly better performance for the direct approach. (3) The added value of the high-resolution experiments (CORDEX-CORE) compared to their coarser-resolution counterparts (CORDEX) is not apparent in this study.

Comparative ecosystem modelling of dynamics and stability of subtropical estuaries under external perturbations in the Gulf of Mexico

Abstract Human intervention and climate change jointly influence the functions and dynamics of marine ecosystems. Studying the impacts of human and climate on ecosystem dynamics is challenging. Unlike experimental studies, research on natural systems is not amendable at the scale of time, space, and biology. With confounding factors well balanced for two adjacent subtropical estuaries except urbanized disturbances, we conducted ecosystem modelling using indirect reasoning by exclusion to quantify the relative impacts of human disruption on estuarine ecosystems under climate variability. One major finding of this study is that the human intervention tends to magnify species fluctuations, complicate the species interaction network, and enhance species interaction strength combined with disclosed downscaling climate effects (indexed as North Atlantic Oscillation and Atlantic Multi-decadal Oscillation) on estuarine hydrology and biological communities. In addition, functional groups appeared to respond more diversely to external forcing in company with human interventions. While human perturbation was shown to destabilize the estuarine ecosystems, making them vulnerable to environmental variability under climate change, buffering effects of species diversity and trophic interaction tend to underpin the ecosystem functions. The findings of this study contribute to the holistic assessment and strategic management of estuarine ecosystems subjected to human and natural disturbances in the face of climate change.

Critical climate thresholds for fire in wet, temperate forests

Climate models predict more frequent droughts and more severe fire weather. Wildfires in wet forests, while historically uncommon, can have catastrophic impacts on forest values and services. Therefore, it is important to ask whether climate change is increasing the frequency of fires in wet forests. Long-term fire histories and weather records were compared in wet Eucalyptus forests supplying water to Melbourne, Australia, to identify the combination of dryness and fire weather under which stand-replacing fires occur. The effect of climate change on stand replacement frequency was predicted using down-scaled regional climate change projections for RCP4.5 and RCP8.5.An index of surface soil dryness (SSD), which is a proxy for live and dead fuel moisture, and daily maximum vapor pressure deficit (Dmax), which is a proxy for daily fire weather, were used to rank each day of each fire season from 1900/01 to 2019/20, within 898 km2 of water supply catchments. The two indices were compared for days with and without stand-replacing fire activity within the study area. Threshold values for the combination of both indices were identified. Damaging fires occurred on about 10 % of days falling above this threshold. The two worst fires, between them burning 86 % of the wet forest in the study area, occurred on days with moderate rather than severe SSD (within the top 2 % of days but only about half of the maximum SSD) but with the most extreme Dmax (highest and second highest ranked out of 43,920 days).The frequency of long fire seasons (SSD > 65 mm for 30 or more days) increased from 1 in ∼30 years during the 20th century to 1 in 4 years in the past 15 years, due to a doubling in the number of warm dry days (Dmax > 2.0 kPa) per fire season. The frequency of extreme fire weather days (Dmax > 5.5 kPa) was also higher in the past 15 years than for most of the previous century. These observations suggest that fire frequency and severity are likely to increase in these wet forests, potentially threatening a suite of ecosystem services. Based on regional climate change projections, increases in the frequency of stand replacing fire will be driven more by increases in maximum temperature than by reductions in rainfall. Under RCP4.5, stand replacing fire frequency in the study area could increase from 1 in ∼140 years historically to 1 in ∼40 years by 2050 and 1 in ∼22 years by 2090 (1 in ∼6 years under RCP8.5), which would result in widespread loss of these iconic forests, including Eucalyptus regnans, the world’s tallest Angiosperm.

Evaluation of multi‐RCM ensembles for simulating spatiotemporal variability of Asian summer monsoon precipitation in the CORDEX‐East Asia Phase 2 domain

AbstractFive regional climate models (RCMs)—CCLM, RegCM, HadGEM3‐RA, SNURCM and WRF—participating in the Coordinated Regional Climate Downscaling Experiment‐East Asia (CORDEX‐EA) Phase 2 project are evaluated for their ability to simulate spatiotemporal variability in Asian summer precipitation. For this purpose, two dynamical downscaling sets, experiments forced by ERA‐Interim reanalysis data (reproduction experiment) and historical data from three Coupled Model Intercomparison Project 5 (CMIP5) general circulation models (GCMs) (historical experiment) are analysed. The horizontal resolution of the downscaled results is 25 km, and the analysis period is from 1981 to 2005 (25 years). The RCMs show reasonable performance in simulating the spatial and temporal characteristics of summer precipitation in CORDEX‐EA Phase 2 domain. To assess spatiotemporal patterns in Asian precipitation, cyclostationary empirical orthogonal function (CSEOF) analysis is used. In the first mode representing seasonal variations, the model ensemble results of both the reproduction experiment (Rep_ENS) and the historical experiment (His_ENS) simulate the peak times, location of precipitation and progression of the monsoons in Asia reasonably. Their features are greatly influenced by the moisture flux, indicating that the relation between precipitation and 850 hPa moisture flux is depicted well by RCM ensembles. In the second mode, which represents intraseasonal variations, the Rep_ENS depicts the western North Pacific summer monsoon break (WNPSM break) phenomenon similar to the observation. Although the His_ENS simulates the WNPSM break later than the observation, it does present the intraseasonal variation in the East Asian summer monsoon region. Based on these results, the five RCM ensemble results show the ability to capture spatiotemporal characteristics of summer precipitation in CORDEX‐EA Phase 2 domain, as well as added value from dynamical downscaling.

Selecting regional climate models based on their skill could give more credible precipitation projections over the complex Southeast Asia region

This study focuses on future seasonal changes in daily precipitation using Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiments-Southeast Asia ensemble (CORDEX-SEA). Projections using this RCM ensemble generally show a larger inter-model spread in winter than in summer, with higher significance and model agreement in summer over most land areas. We evaluate how well the RCMs simulate climatological precipitation using two skill metrics. To extract reliable projections, two sub-ensembles of ‘better’ and ‘worse’ performing models are selected and their respective projections compared. We find projected intensification of summer precipitation over northern SEA, which is robust across RCMs. On the contrary, in the southern part of SEA, the ‘worse’ ensemble projects a significant and widespread decrease in summer rainfall intensity whereas a slight intensification is projected by the ‘better’ ensemble. Further exploration of inter-model differences in future changes reveals that these are mainly explained by changes in moisture supply from large-scale sources (i.e., moisture convergence) with enhanced effects from local sources (i.e., evapotranspiration). The ‘worse’ models project greater changes in atmospheric circulation compared with the ‘better’ models, which can explain part of the uncertainty in projections for daily precipitation over the CORDEX-SEA domain. Hence, our findings might help assess more reliable projections over the SEA region by selecting models based on a two-step model evaluation: the ability of models to simulate historical daily precipitation and their performance in reproducing key physical processes of the regional climate.

Future Projections of EURO‐CORDEX Raw and Bias‐Corrected Daily Maximum Wind Speeds Over Scandinavia

AbstractTwenty historical and future Coordinated Regional Climate Downscaling Experiment ensemble for Europe simulations are bias corrected to investigate the future changes in the daily maximum wind speed over Scandinavia. We use quantile mapping to adjust the wind for the historical period (1985–2014) and quantile delta mapping for two future periods (2041–2070, 2071–2100, RCP8.5) with the 3‐km spatial resolution NORA3 hindcast as the reference data set. Decomposing the variance, we find that most of the inter‐model spread in the bias and the response to climate change is due to the regional climate model over land and mainly to the general climate model over sea. On average, the mean daily maximum wind speed is projected to increase everywhere except along the western coast of Norway and Denmark. In summer, we see an opposite sign over Sweden and Finland. The Norwegian Sea experiences weaker mean and high wind whereas the Baltic Sea experiences a strengthening. Bias correction influences the amplitude of the response, not the response pattern. Wind speed distributions can have different shapes in the future, with, for example, a flattening of the distribution off the coast of Norway with more frequent weak and very strong winds. Apart from a few locations in Norway, we find an increase in the number of days in the local highest historical wind category. Overall, summer exhibits opposite signals compared to the three other seasons. At country scale, the sign of the change in the mean and 98th percentile varies greatly depending on the region and among the simulations, especially for Norway.