Find research datasets worth reusing

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Kortrijk Kennedy Park, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Kortrijk Kennedy Park (50°48'2"N 3°16'13" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Antwerp Berchem, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Uccle KMI, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N, 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven Casa Blanca, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of the Casa Blanca Neighbourhood Leuven (50°52'48"N 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Uccle KMI, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in recent past climate, Leuven City centre, Belgium

Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of city centre of Leuven (50°52'48"N, 4°42'0"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016).  The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the recent past period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).

opencc-by-4.0Oct 2022View details →

How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation? - Observed precipitation data

The dataset contains the rain gauge hourly rainfall series used in the paper "How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?". Each rain gauge series is saved in one Matlab variable, organized as a structure S with five fields: S.name: the identification name of the rain gauge station S.vals_mm: series of hourly rainfall in millimeter S.time_utc: time steps series, in UTC time S.elev_m: elevation of the station, in m a.s.l. S.xy_utm: station coordinates X and Y in meter in the Reference system WGS84/UTM zone 32N

opencc-zeroApr 2024View details →

Data from: Dispersal and connectivity in increasingly extreme climatic conditions

While climate change has been shown to impact several life-history traits of wild-living animal populations, little is known about its effects on dispersal and connectivity. Here, we capitalize on the highly variable flooding regime of the Okavango Delta to investigate impacts of changing environmental conditions on dispersal and connectivity of the endangered African wild dog (Lycaon pictus). Based on remote sensed flood extents observed over 20 years, we derive two extreme flood scenarios: a minimum and a maximum flood extent; representative of very dry and very wet environmental periods. These conditions are akin to those anticipated under increased climatic variability, as it is expected under climate change. Using a movement model parametrized with GPS data from dispersing individuals, we simulate 12,000 individual dispersal trajectories across the ecosystem under both scenarios and investigate patterns of connectivity. Across the entire ecosystem, surface water coverage during maximum flood extent reduces dispersal success (i.e., the propensity of individuals to disperse between adjacent subpopulations) by 12% and increases dispersal durations by 17%. Locally, however, dispersal success diminishes by as much as 78%. Depending on the flood extent, alternative dispersal corridors emerge, some of which in the immediate vicinity of human-dominated landscapes. Notably, under maximum flood extent, the number of dispersing trajectories moving into human-dominated landscapes decreases by 41% at the Okavango Delta's inflow, but increases by 126% at the Delta's distal end. This may drive the amplification of human-wildlife conflict. Whilst predicting the impacts of climate change on environmental conditions on-the-ground remains challenging, our results highlight that environmental change may have significant consequences for dispersal patterns and connectivity, and ultimately, population viability. Acknowledging and anticipating such impacts will be key to effective conservation strategies and to preserve vital dispersal corridors in light of climate change and other human-related landscape alterations.

opencc-zeroJun 2024View details →

Caught out in the cold: Mallard survival decreased during an extreme climatic event

Extreme climatic events (ECEs) can have profound impacts on individual fitness, affecting survival directly or indirectly. Late winter ECEs may be especially detrimental to fitness due to limited food resources and increased energetic requirements during this time. A polar vortex disruption ECE descended upon the mid-continental United States during 7–20 February 2021 with temperatures as low as −29ºC in areas concurrent with ongoing research on mallard (Anas platyrhynchos) movement ecology and survival in Arkansas, Louisiana, and Tennessee, USA spanning winters 2019–2022. Therefore, we opportunistically evaluated the effects of individual characteristics and latitude on daily survival during the ECE. We extended the survival analysis into March to test for lasting effects of the ECE on survival. We tracked 181 GPS-marked mallards during February 2020, 256 in February 2021, and 324 in February 2022. We documented 22 mortalities during the February 2021 ECE (i.e., 9%), but only 6 mortalities during February 2020 (i.e., 2%) and 2022 (i.e., 1%) when conditions were average. February survival (e.g., 28-day survival) during the ECE was 0.908 (85% CI = 0.879–0.937) but was 0.982 (85% CI = 0.973 – 0.991) during the two non-ECE Februaries. The ECE effect on survival was isolated to February and did not affect March survival. Mallards were 5.4 times more likely to die during the ECE in 2021 compared to non-ECE Februaries. Although large-bodied waterfowl appear cold-tolerant and less sensitive to polar vortex disruptions compared to smaller-bodied passerines, direct mortalities can occur if conditions are severe enough and persist, highlighting the need to consider the influence of ECEs on common, seemingly robust species in future global climate change scenarios.

opencc-by-4.0Sep 2024View details →

Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"

<h1>Code and Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"</h1> This dataset accompanies the research paper titled "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates", currently under review for the AGU Journal GRL. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes">Github Repository</a>. The code can also be found as a ".zip" file: *On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes-main.  <h2>Aims</h2> Our study focuses on two important gaps in the literature regarding the extrapolation of empirical downscaling algorithms. First, we examine how well relationships learned from a historical period extrapolate to future unobserved climates. We compare two widely used algorithms, a GAN and a deterministic CNN baseline, that use a similar architecture (i.e. convolutional layers) trained in a model-as-truth framework to downscale daily precipitation over New Zealand. We evaluate their accuracy in capturing climate change signals in mean and extreme precipitation. Second, we explore whether training on future vs. only historical periods combined with different-sized training datasets can improve extrapolation skill.  <h2>Geographic Focus</h2> Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).   <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> The training data used in this study (for our RCM emulator) spans the historical period and future period (SSP370) of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables.  <ul> <li> Resolution: The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation.  </li> <li> Period Coverage: <ul> <li>Training Data: 1960-2100 (Depending on Experiment, see Table 1 for list of experiment configurations)</li> <li>Validation Data: 1985-2014 + 2070-2099 (to compute the climate change signal)</li> </ul> </li> <li> Models: <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM, CNRM-CM6-1, AWI-MR-1 </li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> Training Data: <ul> <li>Target/Ground Truth (Y): <code>target_ACCESS-CM2_hist_ssp370_pr.nc</code></li> <li>Predictor (X): <code>predictor_ACCESS-CM2_hist_ssp370.nc</code></li> </ul> </li> <li> Evaluation Data: All other GCMs can be accessed in one single file, predictor and target variables have the dimensions (time, lat, lon, GCM). <ul> <li>Target/Ground Truth (Y): <code>Other_GCMs_hist_SSP370_target_fields_pr.nc</code></li> <li>Predictor (X): <code>Other_GCMs_hist_SSP370_predictor_fields.nc</code></li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> Regional Climate Model, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>. </li> <li> Predictor and Target Variables: Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository. </li> <li> Training Framework: Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a> </li> </ul> <table> <tbody> <tr> <td> Algorithm </td> <td> Training Data </td> <td> Period </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Historical </td> <td> 1960-2014 (~21,000 days) </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Future (SSP370) </td> <td> 2044-2099 (~21,000 days) </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Historical and Future (SSP370) </td> <td> 1960-2099 (~51,000 days) </td> </tr> <tr> <td> Residual GAN </td> <td> Historical </td> <td> 1960-2014 </td> </tr> <tr> <td> Residual GAN </td> <td> Future (SSP370) </td> <td> 2044-2099 </td> </tr> <tr> <td> Residual GAN </td> <td> Historical and Future (SSP370) </td> <td> 1960-2099 </td> </tr> </tbody> </table> Table 1: The six RCM emulator experiments performed in this study.

opencc-zeroDec 2018View details →

Data from: Do precipitation extremes drive growth and migration timing of a Pacific salmonid fish in Mediterranean‐climate streams?

Climate change is expected to increase weather extremes and variability, including more frequent weather whiplashes or extreme swings between severe drought and extraordinarily wet years. Shifts in precipitation patterns will alter stream flow regimes, affecting critical life history stages of sensitive aquatic organisms. Understanding how threatened fish species, such as steelhead/rainbow trout (Oncorhynchus mykiss), are affected by stream flows in years with contrasting environmental conditions is important for their conservation. Here, we report how extreme wet and dry years, from 2015 to 2018, affected stream flow patterns in two tributaries to the South Fork Eel River, California, USA, and aspects of O. mykiss ecology, including over‐summer fish growth and body condition as well as spring out‐migration timing. We found that stream flow patterns differed across years in the timing and magnitude of large winter–spring flow events and in summer low‐flow levels. We were surprised to find that differences in stream flows did not impact growth, body condition, or timing of out‐migration of O. mykiss. Fish growth was limited in the late summer in these streams (average of 0.02 ± 0.05 mm/d), but was similar across dry and wet years, and so was end‐of‐summer body condition and pool‐specific biomass loss from the beginning to the end of the summer. Similarly, O. mykiss migrated out of tributaries during the last week of March/first week of April regardless of the timing of spring flow events. We suggest that the muted response to inter‐annual hydrologic variability is due to the high quality of habitat provided by these unimpaired, groundwater‐fed tributaries. Similar streams that are likely to maintain cool temperatures and sufficient base flows, even in the driest years, should be a high priority for conservation and restoration efforts.

opencc-zeroSep 2021View details →

Extreme drought reduces climatic disequilibrium in dryland plant communities

High rates of climate change are currently exceeding many plant species' capacity to keep up with climate, leading to mismatches between climatic conditions and climatic preferences of the species present in a community. This disequilibrium between climate and community composition could diminish, however, when critical climate thresholds are exceeded, due to population declines or losses among the more mismatched species. Here, we assessed the effect of an extreme drought event on rich semiarid shrubland communities in the south-eastern Iberian Peninsula. Using a community climate framework, we compared the community climatic disequilibrium before and after the drought episode on three study sites with different levels of precipitation. Disequilibrium was estimated as the difference between observed reference climate and community-inferred climate, calculated as the mean climatic optimum for the species present, weighted by their abundances. We found that extreme drought embedded within a decadal trend of increasing aridity led to a significant reduction in community climatic disequilibrium, and that this reduction was positively related to water deficit (low P/PET values). In contrast, microhabitat variables such as vegetation cover or slope, did not emerge as significant predictors of changes in community climatic disequilibrium. Our study highlights that extreme drought events pushing communities in the same direction as climate trends may decrease community climatic mismatch, leading to communities more adapted to aridity through loss of drought-sensitive species. These findings underscore that extreme events will play a crucial role in speeding up climate-induced community transformations and biodiversity losses.

opencc-by-4.0Sep 2021View details →

Data for: Ecological impacts of extreme climatic events on terrestrial and freshwater biota in the Arctic: A synthesis of current scientific evidence and opportunities

File contains the results of a literature review on the ecological impacts of extreme climatic events on terrestrial and freshwater biota in the Arctic.

opencc-by-4.0Oct 2021View details →

Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"

Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"

opencc-zeroNov 2022View details →

CESM 1.2 climate model simulation output for: The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model 'Wind Stress Shaving' Experiments

Westerly wind bursts (WWBs)—brief but strong westerly wind anomalies in the equatorial Pacific—are believed to play an important role in El Niño Southern Oscillation (ENSO) dynamics, but quantifying their effects is challenging. Here, we investigate the cumulative effects of WWBs on ENSO characteristics, including the occurrence of extreme El Niño events, via modified coupled model experiments within Community Earth System Model (CESM1) in which we progressively reduce the impacts of wind stress anomalies associated with model-generated WWBs. In these "wind stress shaving" experiments we limit momentum transfer from the atmosphere to the ocean above a preset threshold, thus "shaving off" wind bursts. To reduce the tropical Pacific mean state drift, both westerly and easterly wind bursts are removed, although the changes are dominated by WWB reduction. As we impose progressively stronger thresholds, both ENSO amplitude and the frequency of extreme El Niño decrease, and ENSO becomes less asymmetric. The warming center of El Niño shifts westward, indicating less frequent and weaker Eastern Pacific (EP) El Niño events. Removing most of wind bursts-related wind stress anomalies reduces ENSO amplitude by 22%. The essential role of WWBs in the development of extreme El Niño events is revealed in the suppressed eastward migration of the western Pacific warm pool and hence a weaker Bjerknes feedback under wind shaving. Overall, our results reaffirm the importance of WWBs in shaping the characteristics of ENSO and its extreme events and imply that WWB changes with global warming could influence future ENSO.

Data supporting: Impacts of extreme climatic events on trophic network complexity and multidimensional stability

Data used to produce the results presented in the manuscript entitled "Impacts of extreme climatic events on trophic network complexity and multidimensional stability", published in the journal "Ecology". The data were obtained from an outdoor pond mesocosm experiment where freshwater communities were exposed to two different heatwave scenarios.