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Ambient conditions shape microbiome responses to both short- and long-duration environment changes through processes including physiological acclimation, compositional shifts, and evolution. Thus, we predict that microbial communities inhabiting locations with larger diel, episodic, and annual variability in temperature and pH should be less sensitive to shifts in these climate-change factors. To test this hypothesis, we compared responses of surface ocean microbes from more variable (nearshore) and more constant (offshore) sites to short-term factorial warming (+3 °C) and/or acidification (pH -0.3). In all cases, warming alone significantly altered microbial community composition, while acidification had a minor influence. Compared with nearshore microbes, warmed offshore microbiomes exhibited larger changes in community composition, phylotype abundances, respiration rates, and metatranscriptomes, suggesting increased sensitivity of microbes from the less-variable environment. Moreover, while warming increased respiration rates, offshore metatranscriptomes yielded evidence of thermal stress responses in protein synthesis, heat shock proteins, and regulation. Future oceans with warmer waters may enhance overall metabolic and biogeochemical rates, but they will host altered microbial communities, especially in relatively thermally stable regions of the oceans. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2019) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-10-20.

Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Earth_Heat_Inventory_Ocean_Heat_Content_data.nc” contains a consistent long-term Earth system heat inventory over the period 1960-2020. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory published in von Schuckmann et al. (2020), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2020. The dataset also contains estimates for global ocean heat content over 1960-2020 for different depth layers, i.e., 0-300m, 0-700m, 700-2000m, 0-2000m, 2000-bottom, which are described in von Schuckmann et al. (2022). This version includes an update of heat storage of global ocean heat content, where one additional product (Li et al., 2022) had been included to the initial estimate. The Earth heat inventory had been updated accordingly, considering also the update for continental heat content (Cuesta-Valero et al., 2023).

# Data and code for Does increasing temperature accentuate disease impacts on fisheries species? A meta-analysis [https://doi.org/10.5061/dryad.4j0zpc8jx](https://doi.org/10.5061/dryad.4j0zpc8jx) Update November 12, 2024: Updated colors in TM1R plot*, updated plot labels in Salmoniformes*_figures plot, renamed files to be more reflective of figure descriptions in manuscript. Updated names of files at the end of the READ ME document. ## Description of the data and file structure The attached csv file is the compiled dataset used to perform the meta-analysis described in the manuscript. These data include columns not utilized in the text as these categorical variables were later simplified to increase sample size. These columns were retained in this dataset for transparency purposes. Sources for additional information outside of what was provided in the original studies are described in Appendix S2 and full citations are available in Appendix S4. The column descriptions are as follows: Study: In-text citation for the original manuscript where the mortality data were sourced (See Appendix S2 and S4) Group: the experiment associated with that row of mortality data (see Methods) Temp_C: the temperature at which the experiment was performed in degrees Celsius. Temp_Cent: mean-centered temperature in degrees Celsius. Days_in_study: the duration of the experiment in days. TrueLOR: the calculated log odds ratio from that experiment (see Methods) TrueLORVar: the calculated variance of the log odds ratios (see Methods) Inv_var: inverse of the TrueLORVar variance, used to weight Bayesian model (see Methods) Order: Order of the host species used Class: Class of the host species used Phylum: Phylum of the host species used Superfamily: Superfamily of the host species used Host_mobility: If adult host was mobile in the water column (See Appendix S1) Vertebrae: If adult host has a vertebrae (See Appendix S1) LH_clean: Life stage listed in source paper (See Appendix S1) Temp_zone: Host distribution (See Appendix S1) Salinity: Salinity tolerance of host (See Appendix S1), later simplified into Salinity_simple which was the variable used in the meta-analysis. Parasite_Type: Taxonomic group of Parasite used (See Appendix S1), later simplified into Parasite_Type_simple which was the variable used in the meta-analysis. Host_source: The local source of the experimental animals as described in the paper (See Appendix S1), later simplified into Host_source_simple which was the variable used in the meta-analysis. Motivation_code_2: The motivation of the researchers performing the original study (See Appendix S1). Salinity_simple: Simplified salinity tolerance (See Methods, Table 1, and Appendix S1). LH_simple: Life history of the hosts simplified (See Methods, Table 1, and Appendix S1). Parasite: The parasite used in the study (Appendix S2). Parasite_Type_simple: The simplified parasite taxonomy used in the study (See Methods, Table 1, and Appendix S1). Parasite_transmission3: The mode of transportation of the parasite (See Methods, Table 1, and Appendix S1). Pathogen_type: The life history strategy of the parasite (See Methods, Table 1, and Appendix S1). Parasite_location: If the parasite was an external or internal parasite (See Methods, Table 1, and Appendix S1). Parasite_Transmission_simple: Simplified parasite transmission into single or multiple transmission modes. Not used in the meta-analysis Host_source_simple: Simplified Host source (See Methods, Table 1, and Appendix S1). ## Sharing/Access information Data was derived from the sources listed in Appendix S3 and Appendix S4 in the manuscript. ## Code/Software Attached are R scripts to produce the statistical models and all figures in the manuscript. These were created using R version 4.3.1 (2023-06-16 ucrt) -- "Beagle Scouts" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 (64-bit) Final_mods.R : Script with statistical models referenced in paper Host_taxonomony_mod_figure.R: Script that produces Figure 2 and model estimates listed in Table S1. TM1_R_figures2.R: Script to produce model output in Table S2 and Figure 3. Salmoniformes_figures.R: Script to produce model output in Table S3 and Figure 4. Funnel_plot: Script used to produce Figure S2. We compiled data from experimental studies on fisheries species that compared mortality of parasitized and unparasitized hosts at a static temperature. We defined fisheries species to include both invertebrate and vertebrate species that are harvested commercially or recreationally. In Fall 2019, we searched Web of Science following PRISMA protocols (O’Dea et al. 2021) using key terms that would return papers focused on harvested aquatic species, parasites, and diseases, but would exclude papers that were focused on human, environmental or domestic animal health (see Appendix S1 in Supporting Information). This search yielded 1,201 papers. We then screened the abstracts of these papers, and retained only papers that satisfied four criteria: 1) an experiment was performed that included at least one parasite exposure treatment paired with an unexposed control group, 2) temperatures were intended to be constant and not intentionally varied, 3) hosts were from species that constitute a fishery, including those in aquaculture, and 4) estimates of survival or mortality were reported for both infected and uninfected hosts at each temperature treatment. This selection process reduced the number of studies to 386 (Appendix S1 and Figure S1). We obtained full versions of 364 papers (22 papers from the original 386 were unobtainable). We then screened the full text of these papers to ensure a match to our four criteria, which reduced the 364 papers to 70. To increase statistical power to estimate the effect of host Order on parasite-induced mortality, we excluded experiments from hosts in Orders with fewer than three effect sizes. This reduced the number of papers included in our study from 70 to 56 and yielded a total of 287 effect sizes from 131 experiments (several papers included more than one experiment; Appendix S1 and S2, Figure S1). At least two people extracted data from each paper to reduce extraction error. If extracted values differed, the data were re-extracted until there was agreement between the two extractors. For data that were displayed in a graphical format only, we used WebPlotDigitizer (Rohatgi 2022) to extract data. Data (which may have been presented as mortality rates, or proportion surviving) were converted to numbers of host individuals that were dead and alive at the end of the experiment. We also extracted information about the paper itself, including the source of the hosts used in the paper and the motivation for conducting the experiment (see Appendix S1). Finally, we collected additional information about host and parasite traits from outside sources (e.g., other peer reviewed papers, government reports) when necessary to obtain moderator variables (Table 1, Appendix S1 and S2). The moderators (Table 1) were used to test a priori hypotheses regarding how host, parasite, and study design traits influenced how temperature affected parasite-induced mortality. Because our focus was on parasite-induced mortality, we used log odds ratios and the variance surrouding log odds ratio as our effect size to compare host survival in the parasitized vs unparasitized treatments. Rapid warming could drastically alter host-parasite relationships, which is especially important for fisheries crucial to human nutrition and economic livelihoods; yet we lack a synthetic understanding of how warming influences parasite-induced mortality in these systems. We conducted a meta-analysis using 287 effect sizes from 56 empirical papers on harvested aquatic species and determined the relationship between parasite-induced host mortality and temperature and how this relationship was altered by host, parasite and study design traits. Overall, temperature increased parasite-induced host mortality; however, the magnitude and sometimes direction of this relationship varied. Hosts from the order Salmoniformes experienced a greater increase in parasite-induced mortality with temperature than average. Opportunistic parasites were correlated with a greater increase in host mortality with temperature than average, while bacterial parasite-induced mortality was lower than average as temperature increased. Thus, parasites will generally increase host mortality as the environment warms; however, this effect will vary among systems.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CSIRO-ARCCSS.ACCESS-CM2.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Australian Community Climate and Earth System Simulator Climate Model Version 2 climate model, released in 2019, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science). Mailing address: CSIRO, c/o Simon J. Marsland, 107-121 Station Street, Aspendale, Victoria 3195, Australia (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

This Renewable Energy Data Book for 2010 provides facts and figures on energy in general, renewable electricity in the United States, global renewable energy development, wind power, solar energy, geothermal power, biopower, hydropower, advanced water power, hydrogen, renewable fuels, and clean energy investments.

Abstract. This paper aims to assess the spatial variability in the response of CO2 exchange to irradiance across the Arctic tundra during peak season using light response curve (LRC) parameters. This investigation allows us to better understand the future response of Arctic tundra under climatic change. Peak season data were collected during different years (between 1998 and 2010) using the micrometeorological eddy covariance technique from 12 circumpolar Arctic tundra sites, in the range of 64–74° N. The LRCs were generated for 14 days with peak net ecosystem exchange (NEE) using an NEE–irradiance model. Parameters from LRCs represent site-specific traits and characteristics describing the following: (a) NEE at light saturation (Fcsat), (b) dark respiration (Rd), (c) light use efficiency (α), (d) NEE when light is at 1000 μmol m−2 s−1 (Fc1000), (e) potential photosynthesis at light saturation (Psat) and (f) the light compensation point (LCP). Parameterization of LRCs was successful in predicting CO2 flux dynamics across the Arctic tundra. We did not find any trends in LRC parameters across the whole Arctic tundra but there were indications for temperature and latitudinal differences within sub-regions like Russia and Greenland. Together, leaf area index (LAI) and July temperature had a high explanatory power of the variance in assimilation parameters (Fcsat, Fc1000 and Psat, thus illustrating the potential for upscaling CO2 exchange for the whole Arctic tundra. Dark respiration was more variable and less correlated to environmental drivers than were assimilation parameters. This indicates the inherent need to include other parameters such as nutrient availability, substrate quantity and quality in flux monitoring activities.

In the future, the Baltic Sea ecosystem will be impacted both by climate change and by riverine and atmospheric nutrient inputs. Multi-model ensemble simulations comprising one IPCC scenario (A1B), two global climate models, two regional climate models, and three Baltic Sea ecosystem models were performed to elucidate the combined effect of climate change and changes in nutrient inputs. This study focuses on the occurrence of extreme events in the projected future climate. Results suggest that the number of days favoring cyanobacteria blooms could increase, anoxic events may become more frequent and last longer, and salinity may tend to decrease. Nutrient load reductions following the Baltic Sea Action Plan can reduce the deterioration of oxygen conditions.

AbstractThe Paris Conference of Parties (COP21) agreement renewed momentum for action against climate change, creating the space for solutions for conservation of the ocean addressing two of its largest threats: climate change and ocean acidification (CCOA). Recent arguments that ocean policies disregard a mature conservation research field and that protected areas cannot address climate change may be oversimplistic at this time when dynamic solutions for the management of changing oceans are needed. We propose a novel approach, based on spatial meta‐analysis of climate impact models, to improve the positioning of marine protected areas to limit CCOA impacts. We do this by estimating the vulnerability of ocean ecosystems to CCOA in a spatially explicit manner and then co‐mapping human activities such as the placement of renewable energy developments and the distribution of marine protected areas. We test this approach in the NE Atlantic considering also how CCOA impacts the base of the food web which supports protected species, an aspect often neglected in conservation studies. We found that, in this case, current regional conservation plans protect areas with low ecosystem‐level vulnerability to CCOA, but disregard how species may redistribute to new, suitable and productive habitats. Under current plans, these areas remain open to commercial extraction and other uses. Here, and worldwide, ocean conservation strategies under CCOA must recognize the long‐term importance of these habitat refuges, and studies such as this one are needed to identify them. Protecting these areas creates adaptive, climate‐ready and ecosystem‐level policy options for conservation, suitable for changing oceans.