Carbon Crops Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network and Resilient Economic Agricultural Practices in Morris, Minnesota The overall goal of the Carbon Crop study, established in 2000, was to assess strategies for increasing soil C sequestration including converting to no till systems and including perennial grasses (e.g., switchgrass and big bluestem) Overall, the goal of the study has remained constant, although individual treatments were changed after an incremental soil sampling, in response to new hypotheses and questions. Soil sampling is conducted as treatment changes are implemented. In 2012, two of the perennial grass systems (spring harvest of Switchgrass and Big Bluestem) were changed to corn/soybean rotations, beginning with a soybean entry point, to determine if the SOC accrued under the perennial system was lost by converting to a short annual rotation managed without tillage. The second change made was to compare the productivity between recent and traditional switchgrass cultivars. The final change was conversion of autumn harvest of Big Bluestem treatment replaced with an annual biomass crop – Sorghum-Sudan grass. Soil samples were taken to 1 m in 2000, 2006, 2011, and 2016. Nitrous oxide and carbon dioxide fluxes from the soil were measured from June 2009 through March 2012.

Farming Systems Study for Greenhouse gas Reduction through Agricultural Carbon Enhancement network in Morris, Minnesota Tillage is decreasing globally due to recognized benefits of fuel savings and improved soil health in the absence of disturbance. However, a perceived inability to control weeds effectively and economically hinders no-till adoption in organic production systems in the Upper Midwest, USA. A strip-tillage (ST) strategy was explored as an intermediate approach to reducing fuel use and soil disturbance, and still controlling weeds. An 8-year comparison was made between two tillage approaches, one primarily using ST the other using a combination of conventional plow, disk and chisel tillage [conventional tillage (CT)]. Additionally, two rotation schemes were explored within each tillage system: a 2-year rotation (2y) of corn (Zea mays L.), and soybean (Glycine max [L.] Merr.) with a winter rye (Secale cereale L.) cover crop; and a 4-year rotation (4y) of corn, soybean, spring wheat (Triticum aestivum L.) underseeded with alfalfa (Medicago sativa L.), and a second year of alfalfa. These treatments resulted in comparison of four main management systems CT-2y, CT-4y, ST-2y and ST-4y, which also were managed under fertilized and non-fertilized conditions. Yields, whole system productivity (evaluated with potential gross returns), and weed seed densities (first 4 years) were measured. Across years, yields of corn, soybean and wheat were greater by 34% or more under CT than ST but alfalfa yields were the same. Within tillage strategies, corn yields were the same in 2y and 4y rotations, but soybean yields, only under ST, were 29% lower in the fertilized 4y than 2 yr rotation. In the ST-4y system yields of corn and soybean were the same in fertilized and non-fertilized treatments. Over the entire rotation, system productivity was highest in the fertilized CT-2y system, but the same among fertilized ST-4y, and non-fertilized ST-2y, ST-4y, and CT-4y systems. Over the first 4 years, total weed seed density increased comparatively more under ST than CT, and was negatively correlated to corn yields in fertilized CT systems and soybean yields in the fertilized ST-2y system. These results indicated ST compromised productivity, in part due to insufficient weed control, but also due to reduced nutrient availability. ST and diverse rotations may yet be viable options given that overall productivity of fertilized ST-2y and CT-4y systems was within 70% of that in the fertilized CT-2y system. Closing the yield gap between ST and CT would benefit from future research focused on organic weed and nutrient management, particularly for corn.

This tile layer is intended for use in the web map 'Earliest date of first snow of the season for the United States'.This map shows the earliest first day of snow recorded at thousands of locations in the United States during their period of operation. Map based on an analysis of Global Historical Climatology Network (GHCN) station data by Jared Rennie, NOAA's National Centers for Environmental Information (NCEI). Light colors indicates places where the first recorded snowfall happened early in the season, in July or August. Shades of blue and purple show places where the earliest snow fell between between August and May. While the map shows the earliest date of first snow recorded at a given station, this map should not be interpreted as the “earliest ever” first snow of the season. It is simply the earliest date of first snow at a given station during its period of operation. For a more detailed assessment of the earliest date of first snow, please see this Climate.gov blog post. For more information about the Global Historical Climate Network, please visit the GHCN description page. For access to the data, please visit the GHCN data page at NOAA NCEI.

The Report is being developed by NREL and the GRC, with financial support from the Geothermal Technologies Office of the U.S. DOE and the GRC. It is intended to provide geothermal policymakers, regulators, developers, researchers, and other stakeholders with up-to-date information reflecting the 2019 geothermal power production and district heating markets in the United States. It will also present analysis of the current state of the U.S. geothermal industry and markets for both the power production and district heating sectors, with special consideration of developing power projects. In addition, the report will evaluate the impact of state and federal policy, present current research on geothermal development, and offer a future outlook for the U.S. geothermal industry. Data for the 2020 report have been compiled from previous GEA reports, the U.S. Energy Information Association, and from a GRC industry survey conducted in 2020 via a questionnaire sent to all known companies operating U.S. geothermal power plants or with projects in development. This presentation is a summary of the U.S. power production and developing project data collected for the 2020 report.

These tiles are published and intended for use in the map Historic date of first snow.These base map tiles cover the North American extent and include data which represent the historic date by which there’s a 50% chance at least 0.1” of snow will have accumulated, based on each location’s snowfall history from 1981-2010. Map based on an analysis of the current U.S. Climate Normals by Mike Squires, National Centers for Environmental Information.  White indicates places where there is a year-round chance of snow. Shades of blue and purple show places where the first day of snow historically falls between August 1st and December 31st, while dark gray shows places where, historically, the first snow doesn't take place until January 1st or later. Empty circles showing background gray indicate places where snow is so infrequent that there is not enough data to calculate a statistical first date of snow. While the map shows the historic date of first snow, the actual conditions this year may vary widely from this map because current weather patterns will determine the first snow of the year. For a more detailed assessment of the historic date of first snow, please see this Climate.gov blog post by Deke Arndt, NOAA NCEI scientist. For a broad overview of NOAA's 1981–2010 Climate Normals, see NOAA's 1981-2010 U.S. Climate Normals: An Overview published in the Bulletin of the American Meteorological Society, or for a detailed description of snow Normals, seeNOAA's 1981-2010 U.S. Climate Normals: Monthly Precipitation, Snowfall, and Snow Depth published in the Journal of Applied Meteorology and Climatology.

The University of Alaska Fairbanks (UAF) Alaska Hydrokinetic Energy Research Center was tasked with developing a real-time data telemetry / remote power generation system to monitor frazil ice conditions in the Kvichak River in support of the U.S. Department of Energy funded "Next Generation MHK River Power System Optimized for Performance, Durability and Survivability" project. A real-time telemetry system was requested because of the short time span between the end of the frazil ice season when the instruments would be recovered, limited vessel availability and the project end-date. To meet the project objectives, UAF designed and assembled a remote power/real-time data telemetry system that included an auto start propane generator, a small PV array, a small battery bank and line-of-sight radios as well as two sonar systems to monitor river velocity and water column acoustic backscatter strength. Both sonars included internal batteries for powering the instruments in case of failure of the shore based power system. The sonars, deployed in ~5 m of water on the bed of the Kvichak River, adjacent to the Village of Igiugig, Alaska were tethered to shore via a waterproof armored cable that conveyed power to the subsurface instruments and data from the instruments to the shore based telemetry system. The instruments were programmed to record data internally as well as to transmit data serially over the cables to the shore based system. The system was in-place between November, 2016 and June, 2017. While the real-time data telemetry system was not successful and the remote power generation power system was only partially successful, the system design included sufficient redundant power in the form of internal instrument batteries to enable the collection of nearly three months of overlapping velocity and backscatter data (from November through February) and a record of acoustic backscatter strength spanning the entire ~150 day frazil ice season between November, 2016 and ~April, 2017. This submission includes the overwinter ice study plan, dataset, and final report. The dataset includes modeled water velocity, discharge, and measured water velocity and acoustic backscatter strength in winter 2016-17 from the Kvichak River at the Village of Igiugig, Alaska, USA.

What does the data show? This dataset shows the change in winter average temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, winter is defined as December-January-February. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions.The dataset uses projections of daily average air temperature from UKCP18 which are averaged over the winter period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare winter average temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.PeriodDescription1981-2000 baselineAverage temperature (°C) for the period2001-2020 (recent past)Average temperature (°C) for the period2001-2020 (recent past) changeTemperature change (°C) relative to 1981-20001.5°C global warming level changeTemperature change (°C) relative to 1981-20002°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-20003°C global warming level changeTemperature change (°C) relative to 1981-20004°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Winter Average Temperature Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Winter Average Temperature Change, an average is taken across the 21 year period.We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?These data contain a field for each warming level and the 1981-2000 baseline. They are named 'tas winter change' (change in air 'temperature at surface'), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'tas winter change 2.0 median' is the median value for winter for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tas change winter 2.0 median' is named 'tas_winter_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tas winter change 2.0°C median’ values.What do the 'median', 'upper', and 'lower' values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Winter Average Temperature Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.  Useful linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

What does the data show? This dataset shows the change in winter minimum temperature for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, winter is defined as December-January-February.The dataset uses projections of daily minimum air temperature from UKCP18 which are averaged to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a change (in °C) relative to the 1981-2000 value. This enables users to compare winter minimum temperature trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below.PeriodDescription1981-2000 baselineAverage temperature (°C) for the period2001-2020 (recent past)Average temperature (°C) for the period2001-2020 (recent past) changeTemperature change (°C) relative to 1981-20001.5°C global warming level changeTemperature change (°C) relative to 1981-20002°C global warming level changeTemperature change (°C) relative to 1981-20002.5°C global warming level changeTemperature change (°C) relative to 1981-20003°C global warming level changeTemperature change (°C) relative to 1981-20004°C global warming level changeTemperature change (°C) relative to 1981-2000What is a global warming level?The Winter Minimum Temperature Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming.   The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Winter Minimum Temperature Change, an average is taken across the 21 year period.We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.What are the naming conventions and how do I explore the data?These data contain a field for each warming level and the 1981-2000 baseline. They are named 'tasmin winter change' (change in air 'temperature at surface'), the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. ‘tasmin winter change 2.0 median' is the median value for winter for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'tasmin winter change 2.0 median' is named ‘tasmin_winter_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘tasmin winter change 2.0°C median’ values.What do the 'median', 'upper', and 'lower' values mean?Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Winter Minimum Temperature Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty.‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.  Useful linksFor further information on the UK Climate Projections (UKCP).Further information on understanding climate data within the Met Office Climate Data Portal.

[update 28/03/24 - This description previously stated that the the field “2001-2020 (recent past) change” was a percentage change. This field is actually the difference, in units of mm/day. The table below has been updated to reflect this.]What does the data show? This dataset shows the change in winter precipitation rate for a range of global warming levels, including the recent past (2001-2020), compared to the 1981-2000 baseline period. Here, winter is defined as December-January-February. Note, as the values in this dataset are averaged over a season they do not represent possible extreme conditions. The dataset uses projections of daily precipitation from UKCP18 which are averaged over the winter period to give values for the 1981-2000 baseline, the recent past (2001-2020) and global warming levels. The warming levels available are 1.5°C, 2.0°C, 2.5°C, 3.0°C and 4.0°C above the pre-industrial (1850-1900) period. The recent past value and global warming level values are stated as a percentage change (%) relative to the 1981-2000 value. This enables users to compare winter precipitation trends for the different periods. In addition to the change values, values for the 1981-2000 baseline (corresponding to 0.51°C warming) and recent past (2001-2020, corresponding to 0.87°C warming) are also provided. This is summarised in the table below. Period Description 1981-2000 baseline Average value for the period (mm/day) 2001-2020 (recent past) Average value for the period (mm/day) 2001-2020 (recent past) change Change (mm/day) relative to 1981-2000 1.5°C global warming level change Percentage change (%) relative to 1981-2000 2°C global warming level change Percentage change (%) relative to 1981-2000 2.5°C global warming level change Percentage change (%) relative to 1981-2000 3°C global warming level change Percentage change (%) relative to 1981-2000 4°C global warming level change Percentage change (%) relative to 1981-2000 What is a global warming level? The Winter Precipitation Change is calculated from the UKCP18 regional climate projections using the high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g. decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), whilst this dataset allows for the exploration of greater levels of warming. The global warming levels available in this dataset are 1.5°C, 2°C, 2.5°C, 3°C and 4°C. The data at each warming level was calculated using a 21 year period. These 21 year periods are calculated by taking 10 years either side of the first year at which the global warming level is reached. This time will be different for different model ensemble members. To calculate the value for the Winter Precipitation Change, an average is taken across the 21 year period. We cannot provide a precise likelihood for particular emission scenarios being followed in the real world future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected with current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate as it will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level. What are the naming conventions and how do I explore the data? These data contain a field for each warming level and the 1981-2000 baseline. They are named 'pr winter change', the warming level or baseline, and 'upper' 'median' or 'lower' as per the description below. e.g. 'pr winter change 2.0 median' is the median value for summer for the 2.0°C warming level. Decimal points are included in field aliases but not in field names, e.g. 'pr winter change 2.0 median' is named 'pr_winter_change_20_median'. To understand how to explore the data, refer to the New Users ESRI Storymap. Please note, if viewing in ArcGIS Map Viewer, the map will default to ‘pr winter change 2.0°C median’ values. What do the 'median', 'upper', and 'lower' values mean? Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future. For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, the Summer Precipitation Change was calculated for each ensemble member and they were then ranked in order from lowest to highest for each location. The ‘lower’ fields are the second lowest ranked ensemble member. The ‘higher’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble. This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and higher fields, the greater the uncertainty. ‘Lower’, ‘median’ and ‘upper’ are also given for the baseline period as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past. Useful links For further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal.