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Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009. Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009.

Climate-informed conservation priorities in British Columbia (Version 1.0) Territorial acknowledgement: We respectfully acknowledge that we live and work across diverse unceded territories and treaty lands and pay our respects to the First Nations, Inuit and Métis ancestors of these places. We honour our connections to these lands and waters and reaffirm our relationships with one another. Suggested citation: Stolar, J., D. Stralberg, I. Naujokaitis-Lewis, S.E. Nielsen, and G. Kehm. 2023. Spatial priorities for climate-change refugia and connectivity for British Columbia (Version 1.0). Place of publication: University of Alberta, Edmonton, Canada. doi: 10.5281/zenodo.8333303 Corresponding author: stolar@ualberta.ca Summary: The purpose of this project is to identify spatial locations of (a) vulnerabilities within British Columbia’s current network of protected areas and (b) priorities for conservation and management of natural landscapes within British Columbia under a range of future climate-change scenarios. This involved adaptation and implementation of existing continental- and provincial-scale frameworks for identifying areas that have potential to serve as refugia from climate change or corridors for species migration. Outcomes of this work include the provision of practical guidance for protected areas network design and vulnerabilities identification under climate change, with application to other regions and jurisdictions. Project results, in the form of multiple spatial prioritization scenarios, may be used to evaluate the resilience of the existing protected area network and other conservation designations to better understand the risks to British Columbia’s biodiversity in our changing climate. Description: These raster layers represent different scenarios of Zonation rankings of conservation priorities for climate resilience and connectivity between current and 2080s conditions for a provincial-scale analysis. Input conservation features included metrics of macrorefugia (forward and backward climate velocity (km/year), overlapping future and current habitat suitability for ~900 rare species in BC), microrefugia (presence of old growth ecosystems, drought refugia, glaciers/cool slopes/wetlands, and geodiversity), and connectivity. Please see details in the accompanying report. File nomenclature: .zip folder (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.zip): Contains the files listed below. Macrorefugia (2080s_macrorefugia.tif): Scenarios for each taxonomic group (equal weightings for all species) (Core-area Zonation Function) Climate-type velocity + species scenarios from above (Core-area Zonation; equal weightings) Microrefugia (microrefugia.tif): Scenario with old growth forest habitat, landscape geodiversity, wetlands/cool slopes/glaciers, drought refugia (Core-area Zonation; equal weightings) Overall scenario (2080s_macro_micro_connectivity.tif): Inputs from above (with equal weightings) + connectivity metrics (each weighted at 0.1) (Additive Benefit Function Zonation) Conservation priorities (Conservation_priorities_2080s.tif): Overall scenario from above extracted to regions of low human footprint. Restoration priorities (Restoration_priorities_2080s.tif): Overall scenario from above extracted to regions of high human footprint. Accompanying report (Stolar_et_al_2023_CiCP_Zenodo_upload_Version_1.0.pdf): Documentation of rationale, methods and interpretation. READ_ME file (READ_ME_PLEASE.txt): Metadata. Legend interpretation: Ranked Zonation priorities increase from 0 (lowest) to 1 (highest). Raster information: Columns and Rows: 1597, 1368 Number of Bands: 1 Cell Size (X, Y): 1000, 1000 Format: TIFF Pixel Type: floating point Compression: LZW Spatial reference: XY Coordinate System: NAD_1983_Albers Linear Unit: Meter (1.000000) Angular Unit: Degree (0.0174532925199433) false_easting: 1000000 false_northing: 0 central_meridian: -126 standard_parallel_1: 50 standard_parallel_2: 58.5 latitude_of_origin: 45 Datum: D_North_American_1983 Extent: West -139.061502 East -110.430823 North 60.605550 South 47.680823 Disclaimer: The University of Alberta (UofA) is furnishing this deliverable "as is". UofA does not provide any warranty of the contents of the deliverable whatsoever, whether express, implied, or statutory, including, but not limited to, any warranty of merchantability or fitness for a particular purpose or any warranty that the contents of the deliverable will be error-free. Funding: We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation. We gratefully acknowledge the financial support of Environment and Climate Change Canada, the Province of British Columbia through the Ministry of Water, Land and Resource Stewardship) and the Ministry of Environment and Climate Change Strategy, the BC Parks Living Lab for Climate Change and Conservation, and the Wilburforce Foundation.

By using a liquid desiccant ventilation system for dehumidification and an air-handling unit for cooling, the liquid desiccant cooling system (LDCS) system became a promising alternative for traditional technology. Solar thermal energy is suitable to deal with the heat requirement of LDCS in buildings, especially in the areas with abundant solar radiation. The energy saving of solar-assisted liquid desiccant air-conditioning system is significantly affected by various operation conditions, and multi-parameter optimization was necessary to improve the system applicability. In this paper, we investigated the impact of five main parameters on the system performance via self-developed system modelling, including the solution mass flow rate, concentration, cooling tower flow rate, and solar water flow rate and installation area of solar collector. A typical commercial building in Hong Kong was selected as a case study, which air-conditioning load was obtained by Energy-plus. The results indicated that the installation area of solar collector showed the greatest impact, and the effect of heating water flow rate was also important. The effect of desiccant flow rate was significant, but the influence of solution concentration was slight. Then, the multi-parameter optimization was conducted for obtaining a maximum annual electricity saving rate based on the Multi-Population Genetic Algorithm. The optimized installation area of solar collector was 72 m2, and the heating water flow rate was 0.66 kg/s. The optimized solution flow rate was 0.17 kg/s. The required cooling water flow rate was around 0.8 kg/s.

Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. This dataset contains raw data outputs for Teo et al. (2022), Global Change Biology. Please see the published paper for further details on methods. For enquiries, please contact the corresponding authors: hcteo [at] u.nus.edu or lianpinkoh [at] nus.edu.sg.  Shapefiles can be opened with any GIS program such as ArcMap or QGIS. CSV files can be opened with any spreadsheet program such as Microsoft Excel or OpenOffice.

Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve as a climate data record (CDR), it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI, which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the intermission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the Sea-viewing Wide Field-of-view Sensor, Medium-spectral Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and Ocean and Land Colour Instrument with intermission differences corrected in this work. The fitness of the dataset as a CDR was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records. We are sorry that the data for November 2002 was missing in this upload, and we will fix it in the very next version. If you need it, please kindly contact us at yushujie@sio.org.cn.

Conventional analyses suggest the metabolism of heterotrophs is thermally more sensitive than that of autotrophs, implying that warming leads to pronounced trophodynamic imbalances. However, these analyses inappropriately combine within- and across-taxa trends. We present a novel mathematic framework to separate these, revealing that the higher temperature sensitivity of heterotrophs is mainly caused by within-taxa responses which account for 92% of the difference between autotrophic and heterotrophic protists. This dataset contains both the datasets and R codes of per capita growth rates of autotrophic and heterotrophic protists as well as heterotrophic bacteria and insects. The datasets of per capita growth rates against temperature were compiled from the literature. Experimental data were included if they met the following criteria: at least 3 data points with positive growth rate (µ) and at least 2 unique temperatures at which positive µ were measured. To calculate apparent activation energy, we also removed data points with nonpositive µ and those with temperatures above the optimal growth temperature (defined as the temperature corresponding to the maximal µ). We use the free software R (version 4.2.0) with R packages (foreach, nlme, plyr, dplyr) to analyse these datasets. R codes are also provided.

This database includes a large collection of quality-controlled and homogenized historical snow records measured in the 1951-2001 period in the Central and Southern Apennine Mountains (Italy). Such data have been manually digitized from the Hydrological Yearbooks of the Italian National Hydrological and Mareographic Service (hereafter, NHMS), the institution that managed the hydro-meteorological data collection in Italy from 1917 to 2002. More specifically, the rescued dataset includes the monthly observations of three different variables: · The snow cover duration (SCD), which is defined as total number of days in a given month with snow depth on the ground >=1 cm. This variable is available for 110 stations between 288 and 1430 m above the sea level (ASL). · The number of days with snowfall (NDS), which is total number of days in a given month on which the accumulated snowfall (i.e. the amount of fresh snow with respect to the previous observations) is at least 1 cm. This variable is available for 114 stations between 288 and 1430 m ASL. · The height of new snow (HN), which is defined as the monthly amount of fresh snow (expressed in cm). The monthly value is intended as the sum of daily HN data observed in a determined month. This variable is available for 120 stations between 288 and 1750 m ASL. Note that for HN variable, the data availability is restricted to the period 1971-2001. The considered dataset has been subjected to an accurate quality control consisting of several statistical tests: the gross error test, which flags the data that are above or below acceptable physical limits, the consistency test, which involves an inter-variable check, and the tolerance test, which is focused on the outlier detection. In addition, the homogeneity of the rescued time series has been checked using Climatol method (Guijarro, 2018). The latter is based on the Standard Normal Homogeneity Test (Alexandersson, 1986) for the identification of the breaks and on a linear regression approach for the adjustments (Easterling and Peterson, 1995). Climatol has been also employed for the filling of missing values. The database is structured into three different folders (one for each variable). In a determined folder, the user finds two files, one containing the main information regarding the available stations (code, station name, latitude and longitude (in decimal degrees) and altitude ASL (in m)), the other one the monthly time series for the considered variable. Note that the original data sources of this database, the Hydrological Yearbooks of the NHMS, are freely accessible in printed version (i.e. as scanned images in portable document format) through the Italian Institute for Environmental Protection and Research (ISPRA) website (http://www.bio.isprambiente.it/annalipdf). Additional information about the data rescue processing can be found in the preprint “Historical snowfall measurements in the Central and Southern Apennine Mountains: climatology, variability and trend”, open for discussion in The Cryosphere journal (https://doi.org/10.5194/egusphere-2024-1056). References Alexandersson, H.: A homogeneity test applied to precipitation data, J. Climatol., 6, 661–675, 1986. Easterling, D. R. and Peterson, T.C.: A new method for detecting and adjusting for undocumented discontinuities in climatological time series, International Journal Climatol.,15, 369–377, https://doi.org/10.1002/joc.3370150403, 1995. Guijarro, J. A.: Homogenization of climatic series with Climatol, Climatol manual, https://www.climatol.eu/homog_climatolen.pdf (last access: 15 February 2024), 2018.

Project: IPCC Data Distribution Centre : Supplementary data sets for the Sixth Assessment Report - For the Sixth Assessment Report of the IPCC (AR6) input/source and intermediate datasets underlying the AR6 were collected and long-term archived. This project compliments CMIP6 data subset and snapshot analyzed for the WGI AR6. Summary: This data set contains detailed elements the sea level projections associated with the Intergovernmental Panel on Climate Change Sixth Assessment Report. In particular, it contains relative sea level projections that exclude the background term (representing primarily land subsidence or uplift). It includes probability distributions for all the workflows described in AR6 WGI 9.6.3.2. P-boxes derived from these distributions are available in the sister entry 'IPCC-DDC_AR6_Sup_PBox'. These data may be of use for users who want to substitute their own estimates of the background term. Regional projections can also be accessed through the NASA/IPCC Sea Level Projections Tool at https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool. See https://zenodo.org/communities/ipcc-ar6-sea-level-projections for additional related data sets.