This dataset provides annual raster maps of ecosystem carbon stocks for California, USA. Carbon stock estimates were derived by linking the Land Use and Carbon Scenario Simulator (LUCAS) model and the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of ecosystem carbon stocks for California, USA. Carbon stock estimates were derived by linking the Land Use and Carbon Scenario Simulator (LUCAS) model and the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of transition probability (i.e., land use change or disturbance) for California, USA. Land change transition probabilities were derived from the Land Use and Carbon Scenario Simulator (LUCAS). The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future (2021-2100) time periods. Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of land change transition probability (e.g., urbanization, wildland fire, timber harvest) for California, USA. Land change transition probabilities are from Land Use and Carbon Scenario Simulator (LUCAS) simulations. Each simulation was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future (2021-2100) time periods. Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations per simulation.

This dataset provides annual raster maps of transition probability (i.e., land use change or disturbance) for California, USA. Land change transition probabilities were derived from the Land Use and Carbon Scenario Simulator (LUCAS). The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future (2021-2100) time periods. Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of transition probability (i.e., land use change or disturbance) for California, USA. Land change transition probabilities were derived from the Land Use and Carbon Scenario Simulator (LUCAS). The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future (2021-2100) time periods. Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of historical and projected future land use and land cover (LULC) for California, USA. Changes in LULC over time were simulated using the Land Use and Carbon Scenario Simulator (LUCAS) model. The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of historical and projected future land use and land cover (LULC) for California, USA. Changes in LULC over time were simulated using the Land Use and Carbon Scenario Simulator (LUCAS) model. The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides annual raster maps of historical and projected future land use and land cover (LULC) for California, USA. Changes in LULC over time were simulated using the Land Use and Carbon Scenario Simulator (LUCAS). Simulations were run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Projected future simulations were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

This dataset provides tabular data output from a series of modeling simulations for forest ecosystems of the U.S. state of California under different initial conditions scenarios. We used the LUCAS state and transition simulation model with carbon stocks and fluxes based on the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to simulate changes in forest ecosystem carbon balance resulting from historical land use and land cover change, annual climate variability, and disturbance from wildfire and drought-induced forest die-off. The model was run at a 1-km spatial resolution on an annual timestep for the years 1985 to 2020. We simulated 36 initial conditions scenarios based on unique combinations of spatial datasets used to define forest extent, forest composition and forest age at the beginning of the simulation period. Results presented here have been aggregated from the individual grid cell level and summarized for the entire state of California.