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opencc-by-4.0May 2022View details →

Model results based on COSMOS climate model (old version MPI-ESM1)

Nino 3.4 SST of individual models (COSMOS-Nordemg and COSMOS-Tiedtke) and supermodel

opencc-by-4.0Jun 2022View details →

Dataset for Surrogate Model Benchmarking for Dynamic Climate Impact Models

The data represents time series of seasonal weather forecasts for rainfall and temperature. The dataset contains 10 forecasts of 6-month horizon from, two per year, from 2017 to 2021; start dates January 1 and July 1, respectively. Each forecast comprises 50 ensemble members. In total, this sums up to 91300 data points, each containing daily average rainfall, temperature. Each sample (row) comprises following features (columns): <ul> <li>datetime: Date of the forecast sample.</li> <li>forecast: Identifier of the ensemble member, i.e. integer between 1 and total number of ensemblemembers.</li> <li>precip: Averaged daily rainfall forecast in millimeters.</li> <li>temp: Averaged daily temperature forecast in degree Celsius.</li> </ul> Dataset created by The Weather Company, an IBM business. This service is based on data and products of the European Center for Medium-range Weather Forecasts (ECMWF-Archive and ECMWF-RT). Generated using Copernicus Climate Change Service information [2019 and ongoing]. ECMWF Archive data published under a Creative Commons Attribution 4.0 International (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/ Disclaimer: Neither the European Commission nor ECMWF is responsible for any use that may be made of the information it contains.

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Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level

The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for Bombus sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.

opencc-zeroJun 2022View details →

opencc-by-4.0Aug 2022View details →

Multi-model Hydropower Projections for the United States Federal Power Marketing Areas under CMIP5 Climate Change Conditions

This dataset contains an ensemble of monthly hydropower generation projections for the United States Federal Hydropower plants for the periods of 1966-2005 (historical period) and 2011-2050 (future period). The dataset includes the monthly hydropower projections developed in (Kao et al. 2016) based on the Watershed Runoff-Energy Storage (WRES) model and is complemented with another ensemble based on the process-based Water Management Power (WMP) model. The hydrologic projections are estimated through a cascading modeling toolchain that include ten global climate change model projections (ACCESS1-0, BCC-CSM1-1, CCSM4, CMCC-CM, GFDL-ESM2M, MIROC5, MPI-ESM-MR, MRI-CGCM3, NorESM1-M and IPSL-CM5A-LR) under RCP8.5 scenario, which are dynamically downscaled with a regional climate model (RegCM4) ( Pal et al. 2007, Giorgi et al. 2012)), which then inform the Variable Infiltration Capacity (VIC) hydrology model (Liang et al. 1994). The ensemble of hydrologic projections is then informing two processes to translate runoff into hydropower projections. First, WRES models monthly river routing and employs a non-linear statistical approach relating monthly natural flow to hydropower generation, including processes such as spilling. Second, MOSART-WM (Voisin et al. 2013), a large-scale river routing and water management model, provides daily reservoir storage and regulated release at dam locations as well as regulated flow at run-of-the-river power plants. The WMP model then translates reservoir and regulated river dynamics into hydropower projections (Zhou et al. 2018). Those projections are further calibrated to monthly generation provided by the federal utilities. The US federal hydropower plants analyzed in this study include 132 facilities that were built and/or are operated by the US Army Corps of Engineers (USACE), the Bureau of Reclamation (Reclamation), and the International Boundary and Water Commission (IBWC). The electricity generation projected for these hydropower plants were aggregated by four Power Marketing Administrations (PMAs), including Bonneville Power Administration (BPA), Southeastern Power Administration (SEPA), Southwestern Power Administration (SWPA), and Western Area Power Administration (WAPA), and their associate subregions. The two files, SWA9505V2_Gsim_PMA_WRES.mat and SWA9505V2_Gsim_PMA_WMP.mat, represent model outputs from the two hydropower models, WRES and WMP respectively. Each file contains 6 variables: 1) “Models”: the 10 global climate models (GCMs). 2) “PMA_areas”: the 18 subregions of PMAs as defined in (Kao et al. 2015). 3) “PMA_G_mn_6605”:  1966-2005 projected monthly hydropower generation for each PMA sub-regions. Dimension: (12 [months], 40 [years], 18 [subregions], 10 [GCMs]). Unit: MWH. 4) “PMA_G_mn_1150”:  Same as “PMA_G_mn_6605”, but for 2011-2050 projected hydropower generation. 5) “PMA_G_yr_6605”:  1966-2005 projected annual hydropower generation. Dimension: (40 [years], 18 [subregions], 10 [GCMs]) . Unit: MWH. 6) “PMA_G_yr_1150”:  Same as “PMA_G_yr_6605”, but for 2011-2050 projected hydropower generation. The following journal paper details the method in creating the dataset: Impacts of Climate Change on Subannual Hydropower Generation: A Multi-model Assessment of the United States Federal Hydropower Plants Zhou et al. (2022) Preparing for submission to Environmental Research Letters.

opencc-by-4.0Sep 2022View details →

Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice"

Model output data for Smith et al., "Effects of increasing the category resolution of the sea ice thickness distribution in a coupled climate model on Arctic and Antarctic sea ice", in review in Journal of Geophysical Research-Oceans, 2022. Details on CESM model settings and run setups can be found within the manuscript.

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Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States

We compared climatic relationships to insurance loss across the inland Pacific Northwest region of the United States, using a design matrix methodology, to identify optimum temporal windows for climate variables by county in relationship to wheat insurance loss due to drought. The results of our temporal window construction for water availability variables (precipitation, temperature, evapotranspiration, and the Palmer drought severity index [PDSI]) identified spatial patterns across the study area that aligned with regional climate patterns, particularly with regards to drought-prone counties of eastern Washington. Using these optimum time-lagged correlational relationships between insurance loss and individual climate variables, along with commodity pricing, we constructed a regression-based random forest model for insurance loss prediction and evaluation of climatic feature importance. Our cross-validated model results indicated that PDSI was the most important factor in predicting total seasonal wheat/drought insurance loss, with wheat pricing and potential evapotranspiration having noted contributions. Our overall regional model had a R2 of 0.49 and a RMSE of $30.8 million. Model performance typically underestimated annual losses, with moderate spatial variability in terms of performance between counties.

opencc-zeroOct 2022View details →

opencc-by-4.0Oct 2022View details →

Phanerozoic global climatic fields simulated using the mixed-layer general circulation model FOAM

These files contain the output of Phanerozoic global climate simulations conducted using the “slab” mixed-layer ocean-atmosphere general circulation model FOAM. They are available every 20 Myrs between 540 Ma and 0 Ma, both included. Boundary conditions were adapted to best match each time slice. Continental reconstructions were taken from Scotese and Wright (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). We defined pCO2 after the proxy data compilation of Foster et al. (doi:10.1038/ncomms14845) when available and Krause et al. (dot:10.1038/s41467-018-06383-y) for older time slices. Solar luminosity followed Gough et al. (doi:10.1007/BF00151270). Continental vegetation was set to Modern-like latitudinal bands between 0 Ma and 100 Ma (included), tropical evergreen, broad-leaved forest between 120 Ma and 360 Ma (included), tundra between 380 Ma and 440 Ma (included) and rocky desert afterwards. The orbital configuration was set to null eccentricity and minimum obliquity.  The reader is referred to the associated paper for a full description of the model and boundary conditions. All model output file names use the following pattern: « [age]ebP2_solCgough1981_EccN_pCO2FosterKr_[model_component] _slab.nc", with [age], the age expressed in million years ago, and [model_component] being 'atmos' or 'coupl' (atmospheric component or coupler). For each time slice, the topography-bathymetry data used in FOAM is also provided (« Topobathy_[age]eb_postslarti_cor.nc »).

opencc-by-4.0Jan 2014View details →

Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778

Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.

opencc-by-4.0May 2024View details →

AdriE ocean climate model ensemble for the Adriatic Sea - monthly fields

This dataset contains the monthly-averaged fields of the key physical oceanographic quantities from the AdriE ocean model ensemble. The model runs were carried out by using the ROMS modelling system (Haidvogel et al., 2008) forced by the SMHI-RCA4 Regional Climate Model (Samuelsson et al., 2021), in turn driven by different General Circulation Models. The period is 1987-2099 in the severe RCP8.5 scenario for the climate simulations, whereas the evaluation runs span the period 1987-2010. Each file contains the results for one run. The model implementation and its validation are fully described in a manuscript recently submitted to Ocean Science (Bonaldo, D., Carniel, S., Colucci, R. R., Denamiel, C., Pranic, P., Raicich, F., Ricchi, A., Sangelantoni, L., Vilibic, I., and Vitelletti, M. L.: AdriE: a high-resolution ocean model ensemble for the Adriatic Sea under severe climate change conditions, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1468, 2024).

Figure 6 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 6. Empirical cumulative distribution function (ECDF) of the Predicted error |PE| (cft) in testing period for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 4 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 4. Box-plots of the Predicted error | PE| (cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

Figure 7 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 7. Taylor diagram showing the correlation coefficient between the predicted and observed yields (Blue pine and Silver fir) (cft) and standard deviation for the RF and KRR models.

Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.

TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats

TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.

Fig. 9 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling

Fig. 9. The area of distribution of P. melas for 2050 at annual increment of the temperature measuring 0.03–0.05 ºC: for keys see Fig. 8

Fig. 4 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling

Fig. 4. Presumed range of P. fornicatus in 2050 at mean increase in average temperature to 2100 equaling 2.4 ºC: for keys see Fig. 2

Fig. 5 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling

Fig. 5. Predicted range of P. fornicatus in 2070 at mean increment of 2.4 ºC to 2100: for keys see Fig. 2 ASK BRIG

Fig. 10 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling

Fig. 10. Area of the distribution of P. melas in 2070 at annual increment of the temperature equaling 0.03–0.05 ºC: for keys see Fig. 8

Fig. 1 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling

Fig. 1. Analysis of the accuracy of the model of probable distribution: a – omission and Predicted Area for P. fornicatus: 1 – test data, 2 – training data, 3 – fraction of the initial data presented, 4 – predicted emission; b – trend of the operating curve AUC: 1 – test data, 2 – training data, 3 – random prediction

Fig. 8 in Changes in the range of Pterostichus melas and P. fornicatus (Coleoptera, Carabidae) on the basis of climatic modeling

Fig. 8. Area of distribution of Pterostichus melas:in red the most suitable zones for living are indicated (80–100%), orange – 50–80%, yellow – 20–50%, green – less than 10%, dark blue – 0%