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Evolutionary plant-pollinator responses to anthropogenic land-use change: impacts on ecosystem services

Supplements v7 draft of "Effective radiative forcing in the aerosol-climate model CAM5.3-MARC-ARG", by B. S. Grandey, D. Rothenberg, A. Avramov, Q. Jin, H.-H. Lee, X. Liu, Z. Lu, S. Albani, and C. Wang. Update 2018-10-22: the article has now been accepted for publication in ACP.

year when a certain warming level was reached in CMIP5 and CMIP6 data

This code version was used to generate figures for "Fairness and feasibility in deep mitigation pathways with novel carbon dioxide removal considering institutional capacity to mitigate" If you use this software, please cite it using the metadata from this file.

Scripts to produce figures 1, 5, 6 and 7 of Hernández et al., 2020 (in review) Matlab version: 2019b Date: June 2020 Author: Laia Comas-Bru (ORCID: https://orcid.org/0000-0002-7882-4996 Contact: l.comasbru@reading.ac.uk // laia.comas-bru@ucdconnect.ie See metadata in .m files for further information All data files are available in the “Data” folder. Please, cite their corresponding sources (see references below) ___________________________________________________________________________________________________________ Script "Figure_1A.m" and "Figure_1B.m" Fig.1. (a) Spatial display of the first eigenvector for the gridded winter (December – February) monthly sea-level pressure anomalies (in mb) for the North Atlantic domain—calculated using the Twentieth Century Reanalysis data set (20CRv2c; Compo et al., 2011). Location of the proxy-based records (ice cores, lake sediments, speleothems, tree rings, and marine sediments) employed in this study, using symbols and colours to represent the different types of archives and the reconstructed climate variables. (b) Correlation distribution maps between the winter precipitation and temperature (wPre and wTmp) datasets and the NAO, for the boreal winters (December – February) between 1901 and 2016, calculated using the CRU-TS4.1 global climate dataset (Harris et al., 2014) and the NAO and EA indices from Comas-Bru and Hernández (2018). Positive Spearman rank correlations are shown in red and negative correlations are shown in blue. Location of record used for NAOIP is indicated. ___________________________________________________________________________________________________________ Script "Figure_5.m" Fig 5. Correlation between winter (a) precipitation and (b) temperature at the site of NAOIP (grey square) and each of the other grid cells to see how representative is the site for regional winter climate between 1901 and 2016, calculated using the CRU-TS4.1 global climate dataset (Harris et al., 2014). Positive Spearman rank correlations are shown in red and negative correlations are shown in blue. ___________________________________________________________________________________________________________ Script "Figure_6.m" Fig.6: Teleconnectivity maps of the winter (DJF) monthly sea-level pressure field in the North Atlantic region for the period 1872 – 2009 and different linear combinations of the NAO-EA: a) winters with the NAO and the EA of the same sign; b) all winters; c) winters with the NAO and the EA of the opposite sign. Shaded areas represent Spearman correlations as per the colour bar. Black crosses indicate the location of the highest correlated grid cells. The NAO and the EA indices are the 1st and 2nd empirical orthogonal functions calculated from monthly SLP anomalies over a confined N. Atlantic sector using the Twentieth Century Reanalysis data set (20CRv2c; Compo et al., 2011). ___________________________________________________________________________________________________________ Script "Figure_7.m" Fig. 7: Correlation distribution maps of winter NAO and (a, b) temperature and (c, d) precipitation. (a) and (c) are correlations for the subset of winters where NAO and EA are of opposite sign, whereas (b) and (d) are correlations for the subset of winters where NAO and EA are of the same sign. NAO and EA indices from Comas-Bru and Hernandez (2018) and climate data from the CRU-TS4.01 dataset (Harris et al., 2014). Location of NAOIP is shown as a grey square. ___________________________________________________________________________________________________________ References Comas-Bru, L. & Hernández, A. Reconciling North Atlantic climate modes: revised monthly indices for the East Atlantic and the Scandinavian patterns beyond the 20th century. Earth Syst. Sci. Data 10, 2329–2344 (2018). Compo, G. P. et al. The Twentieth Century Reanalysis Project. Q. J. R. Meteorol. Soc. 137, 1–28 (2011). Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

R code, biotoxin data, and sea surface temperature data to reproduce the results in the manuscript "A Seasonal Early Warning Framework for Harmful Algal Blooms". The manuscript has not been fully peer reviewed, so the code and data may be updated in the future.

First release of moisture tracking analyses to support submitted manuscript "Seasonal Variations in Moisture Origin Explain Spatial Gradients in Precipitation Isotope Seasonality on Coastal Western Greenland" by A. A. Cluett, E. K. Thomas, S. M. Evans, and P. W. Keys.

Description STEMMUS-UEB V1.0.0 coupled the Simultaneous Transfer of Energy, Momentum, and Mass in Unsaturated Soil with Freeze-Thaw (STEMMUS-FT, Zeng et al., 2011a,b; Zeng and Su, 2013; Yu et al., 2018) with snowmelt process model (Utah Energy Balance, UEB, Tarboton and Luce, 1996). With the various representations of soil physical processes, from the basic coupled, to tightly advanced coupled water and heat transfer, and further to the explicit consideration of airflow, STEMMUS-FT model facilitates us to understand and interpret the role of soil physical processes (Zeng et al., 2011a,b; Yu et al., 2018; Yu et al., 2020). UEB model is a physically-based snowmelt model developed by David Tarboton's group. The model uses the lumped representation of snowpack with two primary state variables, snow water equivalent SWE and the internal energy U. Water and energy balance are numerically solved and kept track. UEB uses the physical-based calculations of surface energy exchanges. Melt outflow is regarded as the function of the liquid fraction, by Darcy's law. The latest version, UEBGrid, developed for the gridded application of UEB can be found from https://github.com/dtarb/UEBFortran. Older versions of UEB may be obtained from http://hydrology.usu.edu/dtarb/snow/snow.html. The one-way sequential coupling is employed to couple the soil model (STEMMUS-FT) with the snowpack model (UEB). UEB model takes the atmospheric forcing as the input (precipitation, air temperature, wind speed and direction, relative humidity, shortwave, and longwave radiation) and solves the snowpack energy and mass balance, provides the melt water flux and heat flux as the surface boundary conditions for the soil model STEMMUS-FT. STEMMUS-FT then solves the energy and mass balance equations of soil layers in one timestep. Setup and requirements The code is tested with MATLAB 2019b. STEMMUS-UEB is executed in MATLAB by simply running MainLoop.m after you finish all the model setup and give the input data to STEMMUS-UEB. Several steps are necessary to build up the model setup. Setting the temporal information and model domain; Setting soil properties and snow properties; Setting the initialization condition for soil and snow submodules, respectively; Inputting the meteorological forcing information; Setting the surface/bottom conditions; Then you are ready to run STEMMUS-UEB by running MainLoop.m. {"references": ["Zeng, Y., Su, Z., Wan, L., and Wen, J.: A simulation analysis of the advective effect on evaporation using a two-phase heat and mass flow model, Water Resour. Res., 47, W10529, 10.1029/2011WR010701, 2011a.", "Zeng, Y., Su, Z., Wan, L., and Wen, J.: Numerical analysis of air-water-heat flow in unsaturated soil: Is it necessary to consider airflow in land surface models?, Journal of Geophysical Research: Atmospheres, 116, D20107, 10.1029/2011JD015835, 2011b.", "Yu, L., Zeng, Y., Wen, J., and Su, Z.: Liquid-Vapor-Air Flow in the Frozen Soil, Journal of Geophysical Research: Atmospheres, 123, 7393-7415, 10.1029/2018jd028502, 2018.", "Tarboton, D. G., and Luce, C. H.: Utah Energy Balance Snow Accumulation and Melt Model (UEB), Computer model technical description and users guide, Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station, 1996."]} Codes are free for scientific and educational purposes while users should comply with the fair use policy. Codes are shared in the hope that it will be useful but without any warranty. For any questions, please contact Lianyu Yu (l.yu@utwente.nl), Yijian Zeng (y.zeng@utwente.nl), or Zhongbo Su (z.su@utwente.nl). Authors would like to acknowledge the financial support from the National Natural Science Foundation of China (grant no. 41971033), the Fundamental Research Funds for the Central Universities, CHD (grant no. 300102298307), and the NWO project "Modelling Freeze-Thaw Processes with Active and Passive Microwave Observations" (project ALW-GO/14-29).

The model and input files used to conduct Ou et al. (Deep Mitigation of CO2 and non-CO2 Greenhouse Gases towards 1.5°C and 2°C Futures) The "model" folder contains the version of GCAM5.3 used to reproduce the results The "setup" folder contains the batch files (batch-1p5-pathway.xml and batch-2p0-pathway.xml) used to set up GCAM simulations. Additional policy files are included in subfolders (policy-1p5-pathway and policy-2p0-pathway)