High-resolution Climate Information Research Articles

The CORDEX.be initiative as a foundation for climate services in Belgium

The CORDEX.be project created the foundations for Belgian climate services by producing high-resolution Belgian climate information that (a) incorporates the expertise of the different Belgian climate modeling groups and that (b) is consistent with the outcomes of the international CORDEX (“COordinated Regional Climate Downscaling Experiment”) project. The key practical tasks for the project were the coordination of activities among different Belgian climate groups, fostering the links to specific international initiatives and the creation of a stakeholder dialogue. Scientifically, the CORDEX.be project contributed to the EURO-CORDEX project, created a small ensemble of High-Resolution (H-Res) future projections over Belgium at convection-permitting resolutions and coupled these to seven Local Impact Models. Several impact studies have been carried out. The project also addressed some aspects of climate change uncertainties. The interactions and feedback from the stakeholder dialogue led to different practical applications at the Belgian national level.

Assessment of the Performance of Three Dynamical Climate Downscaling Methods Using Different Land Surface Information over China

This study aims to assess the performance of different dynamical downscaling methods using updated land surface information. Particular attention is given to obtaining high-resolution climate information over China by the combination of an appropriate dynamical downscaling method and updated land surface information. Two group experiments using two land surface datasets are performed, including default Weather Research and Forecasting (WRF) land surface data (OLD) and accurate dynamically accordant MODIS data (NEW). Each group consists of three types of experiments for the summer of 2014, including traditional continuous integration (CT), spectral nudging (SN), and re-initialization (Re) experiments. The Weather Research and Forecasting (WRF) model is used to dynamically downscale ERA-Interim (reanalysis of the European Centre for Medium-Range Weather Forecast, ECMWF) data with a grid spacing of 30 km over China. The simulations are evaluated via comparison with observed conventional meteorological variables, showing that the CT method, which notably overestimates 2 m temperature and underestimates 2 m relative humidity across China, performs the worst; the SN and Re runs outperform the CT method, and the Re shows the smallest RMSE (root means square error). A comparison of observed and simulated precipitation shows that the SN simulation is closest to the observed data, while the CT and Re simulations overestimate precipitation south of the Yangtze River. Compared with the OLD group, the RMSE values of temperature and relative humidity are significantly improved in CT and SN, and there is smaller improved in Re. However, obvious improvements in precipitation are not evident.

Multi-sectoral, high-resolution assessment of climate change consequences of coastal flooding

In the context of growing concern about the threat of flooding posed by climate change in coastal areas, the Spanish plan for coastal adaptation to climate change gave rise to stringent requirements on risk consequence estimates at the regional scale O (100 km). Within this framework, we propose a methodology that combines high space-time resolution climate information (reanalysis databases and projections), local data on exposure that accounts for the most relevant sectors, site-specific vulnerability functions, and flood risk consequence valuation, gridded at 5 m. This approach involves efficient multiple-forcing flood modeling, in which the connection between climate change and potential inundation is primarily established through the definition of a total water level index. This research tackles challenging issues, including the importance of incorporating the effects of existing coastal defenses and local wave effects in port areas, dealing with data at different spatial scales and sectors in an integrated way, and the impact of discounting. The results provide insights into the possible consequences of inaction for a range of future scenarios based on changes in climate and socio-economics over the most relevant sectors. With the goal of prioritizing adaptive action and the efficient assignment of funds, we propose a weight-based integration of the sectoral value-at-risk through the application of Bayesian techniques and expert judgment. The methodology described here was applied to a pilot case study on the coast of Asturias in northern Spain.

Putting trapped populations into place: Climate change and inter-district migration flows in Zambia.

Research shows that the association between adverse climate conditions and human migration is heterogeneous. One reason for this heterogeneity is the differential vulnerability of populations to climate change. This includes highly vulnerable, "trapped" populations that are too poor to migrate given deep and persistent poverty, the financial costs of migrating, and the erosion of already fragile economic livelihoods under climate change. Another reason for this heterogeneity is the differential vulnerability of places. However, despite the growing list of studies showing that the climate-migration relationship clearly varies across places, there is surprisingly little research on the characteristics of places themselves that trap, or immobilize, populations. Accordingly, we provide the first account of the "holding power" of places in the association between adverse climate conditions and migration flows among 55 districts in Zambia in 2000 and 2010. Methodologically, we combine high resolution climate information with aggregated census micro data to estimate gravity models of inter-district migration flows. Results reveal that the association between adverse climate conditions and migration is positive only for wealthy migrant-sending districts. In contrast, poor districts are characterized by climate-related immobility. Yet, our findings show that access to migrant networks enables climate related mobility in the poorest districts, suggesting a viable pathway to overcome mobility constraints. Planners and policy makers need to recognize the holding power of places that can trap populations and develop programs to support in situ adaptation and to facilitate migration to avoid humanitarian emergencies.

Local-scale changes in mean and heavy precipitation in Western Europe, climate change or internal variability?

High-resolution climate information provided by e.g. regional climate models (RCMs) is valuable for exploring the changing weather under global warming, and assessing the local impact of climate change. While there is generally more confidence in the representativeness of simulated processes at higher resolutions, internal variability of the climate system—‘noise’, intrinsic to the chaotic nature of atmospheric and oceanic processes—is larger at smaller spatial scales as well, limiting the predictability of the climate signal. To quantify the internal variability and robustly estimate the climate signal, large initial-condition ensembles of climate simulations conducted with a single model provide essential information. We analyze a regional downscaling of a 16-member initial-condition ensemble over western Europe and the Alps at 0.11° resolution, similar to the highest resolution EURO-CORDEX simulations. We examine the strength of the forced climate response (signal) in mean and extreme daily precipitation with respect to noise due to internal variability, and find robust small-scale geographical features in the forced response, indicating regional differences in changes in the probability of events. However, individual ensemble members provide only limited information on the forced climate response, even for high levels of global warming. Although the results are based on a single RCM–GCM chain, we believe that they have general value in providing insight in the fraction of the uncertainty in high-resolution climate information that is irreducible, and can assist in the correct interpretation of fine-scale information in multi-model ensembles in terms of a forced response and noise due to internal variability.

Climate-induced glacier and snow loss imperils alpine stream insects.

Climate warming is causing rapid loss of glaciers and snowpack in mountainous regions worldwide. These changes are predicted to negatively impact the habitats of many range-restricted species, particularly endemic, mountaintop species dependent on the unique thermal and hydrologic conditions found only in glacier-fed and snow melt-driven alpine streams. Although progress has been made, existing understanding of the status, distribution, and ecology of alpine aquatic species, particularly in North America, is lacking, thereby hindering conservation and management programs. Two aquatic insects - the meltwater stonefly (Lednia tumana) and the glacier stonefly (Zapada glacier) - were recently proposed for listing under the U.S. Endangered Species Act due to climate-change-induced habitat loss. Using a large dataset (272 streams, 482 total sites) with high-resolution climate and habitat information, we describe the distribution, status, and key environmental features that limit L.tumana and Z.glacier across the northern Rocky Mountains. Lednia tumana was detected in 113 streams (175 sites) within Glacier National Park (GNP) and surrounding areas. The probability of L.tumana occurrence increased with cold stream temperatures and close proximity to glaciers and permanent snowfields. Similarly, densities of L.tumana declined with increasing distance from stream source. Zapada glacier was only detected in 10 streams (24 sites), six in GNP and four in mountain ranges up to ~600km southwest. Our results show that both L.tumana and Z.glacier inhabit an extremely narrow distribution, restricted to short sections of cold, alpine streams often below glaciers predicted to disappear over the next two decades. Climate warming-induced glacier and snow loss clearly imperils the persistence of L.tumana and Z.glacier throughout their ranges, highlighting the role of mountaintop aquatic invertebrates as sentinels of climate change in mid-latitude regions.

Calibrating regionally downscaled precipitation over Norway through quantile-based approaches

Abstract. Dynamical downscaling of earth system models is intended to produce high-resolution climate information at regional to local scales. Current models, while adequate for describing temperature distributions at relatively small scales, struggle when it comes to describing precipitation distributions. In order to better match the distribution of observed precipitation over Norway, we consider approaches to statistical adjustment of the output from a regional climate model when forced with ERA-40 reanalysis boundary conditions. As a second step, we try to correct downscalings of historical climate model runs using these transformations built from downscaled ERA-40 data. Unless such calibrations are successful, it is difficult to argue that scenario-based downscaled climate projections are realistic and useful for decision makers. We study both full quantile calibrations and several different methods that correct individual quantiles separately using random field models. Results based on cross-validation show that while a full quantile calibration is not very effective in this case, one can correct individual quantiles satisfactorily if the spatial structure in the data are accounted for. Interestingly, different methods are favoured depending on whether ERA-40 data or historical climate model runs are adjusted.

Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania

Spatially high resolution climate information is required for a variety of applications in but not limited to functional biodiversity research. In order to scale the generally plot-based research findings to a landscape level, spatial interpolation methods of meteorological variables are required. Based on a network of temperature observation plots across the southern slopes of Mt. Kilimanjaro, the skill of 14 machine learning algorithms in predicting spatial temperature patterns is tested and evaluated against the heavily utilized kriging approach. Based on a 10-fold cross-validation testing design, regression trees generally perform better than linear and non-linear regression models. The best individual performance has been observed by the stochastic gradient boosting model followed by Cubist, random forest and model averaged neural networks which except for the latter are all regression tree-based algorithms. While these machine learning algorithms perform better than kriging in a quantitative evaluation, the overall visual interpretation of the resulting air temperature maps is ambiguous. Here, a combined Cubist and residual kriging approach can be considered the best solution.

Reconstruction of the Spring 2011 Richelieu River Flood by Two Regional Climate Models and a Hydrological Model

Abstract Climate simulations made with two regional climate models (RCMs), the French Aire Limitée Adaptation Dynamique Développement International (ALADIN) and the Canadian Regional Climate Model, version 5 (CRCM5), operating on 10-km meshes for the period 1989–2011, and the Hydro-Québec hydrological model (HSAMI), are used to reconstruct the spring 2011 Richelieu River flood in the southern region of the province of Québec, Canada. The analysis shows that the simulated fields of 2-m air temperature, precipitation, and snow water equivalent by the RCMs closely match the observations with similar multiyear means and a high correlation of the monthly anomalies. The climatic conditions responsible for the 2011 flood are generally well simulated by the RCMs. The use of multidecadal RCM simulations facilitates the identification of anomalies that contributed to the flood. The flood was linked to a combination of factors: the 2010/11 winter was cold and snowy, the snowmelt in spring was fast, and there was a record amount of precipitation in April and May. Driven by outputs from the RCMs, HSAMI was able to reproduce the mean hydrograph of the Richelieu River, but it underestimated the peak of the 2011 flood. HSAMI adequately computes the water transport from the mountains to the river mouth and the storage effect of Lake Champlain, which dampens the flood over a long period. Overall, the results suggest that RCM simulations can be useful for reconstructing high-resolution climate information and providing new variables that can help better understand the causes of extreme climatic events.

Seawater temperature seasonality in the South China Sea during the late Holocene derived from high-resolution Sr/Ca ratios of Tridacna gigas

Temperature seasonality, the difference between summer and winter temperature, has significant influences on global terrestrial and marine ecosystems. However, most of proxy-based climate records are of limited temporal resolution and thus insufficient to quantify the past temperature seasonality. In this study, high-resolution Sr/Ca ratios of modern (live-caught) and fossil (dead-collected) Tridacna gigas shells from the South China Sea (SCS) were used to reconstruct the seawater temperature seasonality during the late Holocene. The averaged seawater temperature seasonality around 2165±75 BC (4.46±1.41°C, derived from the data of 18yr) were similar to the seasonality of recent decade (4.41±0.82°C during AD 1994–2005), but the temperature seasonality around AD 50±40 (3.69±1.37°C, derived from the data of 48yr) and AD 990±40 (3.64±0.87°C, derived from the data of 11yr) was significantly lower than that during AD 1994–2005. The reduced seasonality around AD 990±40 was attributable to the unusually warm winter during the medieval times, probably caused by the weakening of East Asian Winter Monsoon. Our study highlighted the potential of T. gigas shells in providing high-resolution seasonality climate information during the late Holocene.

Evaluation of WRF Mean and Extreme Precipitation over Spain: Present Climate (1970–99)

Abstract The ability of the Weather Research and Forecasting model (WRF) to simulate precipitation over Spain is evaluated from a climatological point of view. The complex topography and the large rainfall variability make the Iberian Peninsula a particularly interesting region and permit assessment of model performance under very demanding conditions. Three high-resolution (10 km) simulations over the Iberian Peninsula have been completed spanning a 30-yr period (1970–99) and driven by different datasets: the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) as “perfect boundary conditions” and two general circulation models (GCMs), the Max Planck Institute ECHAM5 model (ECHAM5/MPI) and the NCAR Community Climate System Model, version 3 (CCSM3). The daily precipitation observational grid Spain02 is employed to evaluate the model at varying time scales. Not only are the long-term means (annual, seasonal, and monthly) examined but also the high-order statistics (extreme events). The WRF provides valuable information on precipitation at high resolution and enhances local spatial distribution due to orographic features. Although substantial errors are still observed in terms of monthly precipitation, especially during the spring, the model is largely able to capture the various precipitation regimes. The major benefits of using WRF are related to the spatial distribution of rainfall and the simulation of extreme events, two facets of climate that can be barely explored with GCMs. This study shows that WRF can be a useful tool for generating high-resolution climate information for Spanish precipitation at spatial and temporal scales that are crucial for both the environment and human life.

Potential for added value in temperature simulated by high-resolution nested RCMs in present climate and in the climate change signal

Regional Climate Models (RCMs) have been developed in the last two decades in order to produce high-resolution climate information by downscaling Atmosphere-Ocean General Circulation Models (AOGCMs) simulations or analyses of observed data. A crucial evaluation of RCMs worth is given by the assessment of the value added compared to the driving data. This evaluation is usually very complex due to the manifold circumstances that can preclude a fair assessment. In order to circumvent these issues, here we limit ourselves to estimating the potential of RCMs to add value over coarse-resolution data. We do this by quantifying the importance of fine-scale RCM-resolved features in the near-surface temperature, but disregarding their skill. The Reynolds decomposition technique is used to separate the variance of the time-varying RCM-simulated temperature field according to the contribution of large and small spatial scales and of stationary and transient processes. The temperature variance is then approximated by the contribution of four terms, two of them associated with coarse-scales (e.g., corresponding to the scales that can be simulated by AOGCMs) and two of them describing the original contribution of RCM simulations. Results show that the potential added value (PAV) emerges almost exclusively in regions characterised by important surface forcings either due to the presence of fine-scale topography or land-water contrasts. Moreover, some of the processes leading to small-scale variability appear to be related with relatively simple mechanisms such as the distinct physical properties of the Earth surface and the general variation of temperature with altitude in the Earth atmosphere. Finally, the article includes some results of the application of the PAV framework to the future temperature change signal due to anthropogenic greenhouse gasses. Here, contrary to previous studies centred on precipitation, findings suggest for surface temperature a relatively low potential of RCMs to add value over coarser resolution models, with the greatest potential located in coastline regions due to the differential warming occurring in land and water surfaces.

The Reproducibility of Surface Air Temperature over South Korea Using Dynamical Downscaling and Statistical Correction

On the Korean peninsula, observation is being conducted so densely that the mean distance between stations of the extensive ground-based data network is only 12.7 km. Nevertheless, because of significant mountainous terrain and the fact that most observation sites are situated in low areas rather than mountain tops or ridges, the detailed topographical effect on temperature distribution is not reflected properly. A model using fine-scale grid spacing can represent such a topographical effect well, but due to systematic biases in the model, simulated temperature distribution will be different from the actual observation. This study therefore attempts to produce a detailed mean temperature distribution for South Korea through a method combining dynamical downscaling and statistical correction. For the dynamical downscaling, the Weather Research and Forecast (WRF) model developed by the U.S. National Center for Atmospheric Research (NCAR) is used. We applied a multi-nesting technique to obtain high-resolution climate information (3 km) with a focus on the Korean peninsula. The integration period was 10 years from January 1999 to December 2008. For the correction of systematic biases shown in downscaled temperature, a perturbation method divided into the mean and the perturbation part was used with a different correction method being applied to each part. The mean was corrected by a weighting function while the perturbation was corrected by the self-organizing maps method, which is one of the artificial neural networks method. The results with correction agree well with the observed pattern compared to those without correction, improving the spatial and temporal correlations as well as the root mean square error. In addition, they represented detailed spatial features of temperature including topographic signals, which cannot be expressed properly by gridded observation. Through comparison with in-situ observation with gridded values after objective analysis, it was found that the detailed structure correctly reflected topographically diverse signals that could not be derived from limited observation data.

The influence of climate on 13C/ 12C and 18O/ 16O ratios in tree ring cellulose of Pinus sylvestris L. growing in the central Scandinavian Mountains

The stable isotope composition of tree rings is known to contain valuable information of past climatic and environmental changes, which may be used as a complement to tree ring width and maximum latewood density in climate reconstructions. In this study we examine the character and strength of the climate signal captured in the δ 18O and δ 13C values of Scots pine ( Pinus sylvestris L.) trees growing at the tree-line in the central Scandinavian Mountains. Between 4 and 14 trees were pooled to produce annual records of carbon and oxygen isotope ratios from tree ring cellulose, spanning the period AD 1736–2006. Weather conditions of the current growing season most strongly influenced the carbon and oxygen isotope ratios in a given tree ring. Both records showed positive correlations with temperature, sunshine and air pressure, and negative associations with precipitation. The overall climate signal in δ 13C appeared to be slightly stronger than that of δ 18O. Spatial correlation analysis with gridded instrumental data demonstrated that the carbon series captured the summer temperature signal in a broad region of mid-west Sweden and the eastern part of Norway, greatly exceeding the spatial coverage of the signal derived from pine ring widths growing in the central Scandinavian mountains. A weak relationship between δ 13C and precipitation and a much stronger temperature and sunshine dependence may imply that photosynthetic rate rather than stomatal conductance is more important in controlling the inter-annual tree ring δ 13C variability in the area. Moreover, it was shown that, the climate– δ 18O and δ 13C relationship was temporally unstable throughout the twentieth century, which was linked to large-scale shifts in climate that may have altered the isotope-climate dependence. Our results thus demonstrate that, stable isotopes in tree rings from maritime high-altitude Scandinavia can provide high-resolution regional climate information, especially regarding parameters associated with temperature. The non-stationary nature of the isotope–precipitation relationship may provide important information on past changes in the large-scale atmospheric circulation during summer. However, these issues needs to be further investigated before pine tree ring isotopes from this region can be confidently used in palaeoclimate reconstructions.

A strategy to improve the contribution of coral data to high-resolution paleoclimatology

Various geochemical tracers measured in massive coral skeletons have the potential to compliment other sources of high-resolution proxy climate information (e.g. tree rings, ice cores) by providing paleoclimatic data from shallow-water tropical ocean regions. Twenty previously published coral records (19 δ 18O and one Sr/Ca series), between 54 and 346 years in length, were examined for their local and larger-scale relationships (El Niño-Southern Oscillation) with climate. Series were examined in a consistent manner over the same time periods and relationships were tested for stability through time. The results demonstrate a spectrum of climatic interpretation ranging from (1) no significant relationship of the coral record with either local or larger-scale climate variables, (2) significant relationship of the coral record with climate but the relationship is not stable through time, and (3) the coral record shows a significant and temporally stable relationship with climate which explains a significant and useful proportion of climate variance. It is concluded that reliance, to date, on analyzing only one long coral core from a site has prevented identification of coral records or parts of coral records where non-climatic factors are dominating the geochemical signal. Various factors may cause these non-climatic signals to dominate. More process studies into how corals of particular species, with various growth rates and from various environmental settings incorporate geochemical tracers into their skeleton are required to understand exactly what is being measured. In addition, it is recommended that some level of replication (ideally, three separate corals) of geochemical tracers will ensure accurate dating of the record, identification of periods when non-climatic factors dominate and thus enhance the reliability of the paleoclimatic record. Such an approach will allow the rich archive of environmental information contained in massive coral skeletons to make a significant contribution to high-resolution paleoclimatology and improve our understanding of the nature and causes of past climate variability.