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4 results for “2010-2100”
LANDIS-II PnET Impacts of Warming on Forest Net Primary Productivity in New England 2010-2100
Climate warming is expected to lengthen growing seasons of temperate forest ecosystems and increase gross primary productivity. Simultaneously, warming is expected to increase summer ecosystem respiration, which could offset gains accrued from longer growing seasons. These responses have been observed during anomalously warm years, but the role of future climate change on phenological trade‐offs and how they affect net primary productivity (NPP) at regional scales in temperate forests remain unexplored. We simulated scenarios of climate change on monthly forest NPP throughout 18 million hectares of temperate forests in New England, USA, through year 2100. Using an ecophysiological model coupled to a forest landscape model, we simulated scenarios of climate change on monthly NPP. A high emission scenario (RCP 8.5), resulted in longer growing seasons that offset midsummer ecosystem respiration costs and produced greater annual NPP throughout the study landscape compared to simulations using the current climate. In spring and autumn months, temperature was positively associated with greater NPP; in summer months, the relationship was negative. Spatially, the greatest increase in NPP occurred in the warmer southern region under a warm climate scenario with increased precipitation. Under a warm scenario with drier conditions, the greatest increase in NPP occurred in the cooler northern region. Phenological trade‐offs will affect NPP of future forests and their potential to serve as a negative feedback to climate change. Barring other limitations, longer growing seasons will offset greater respiratory demands and contribute to increases in NPP throughout the temperate forests of New England in the future.
VEClim's initial assessment of Aedes albopictus activity and disease risk in three decades: historical (1980-1990), recent (2010-2020), and future (2090-2100).
<p><span>This is the initial release of VEClim, an early warning decision support system for climate-sensitive vector activity and vector-borne disease risk assessment, comprising an assessment of the seasonal dynamics of <em>Aedes albopictus</em>, the Asian tiger mosquito, and the associated risk of chikungunya virus transmission. This analysis compares three decades, i.e., historical (1980-1990), </span><span>recent <span>(2010-2020), and future (2090-2100), in terms of seasonal and geospatial averages. </span></span></p>
Reference Climate (1971-2005) and RCP 8.5 scenario (2010-2100) of Destra sele for SWAP simulation
<p>The database refers to the weather files for the simulation run of SWAP model (Kroes et al., 2017) in the Destra Sele area (Regione Campania, southern Italy) under Reference Climate (RC, 1971-2005) and future climate scenario (RCP 8.5, 2010-2100).</p> <p>The future climate scenarios were obtained by using the high resolution regional climate model (RCM) COSMO-CLM (Rockel et al., 2008), with a configuration employing a spatial resolution of 0.0715°(about 8 km), which was optimised over the Italian area. The validations performed showed that these model data agree closely with different regional high-resolution observational datasets, in terms of both average temperature and precipitation in Bucchignani et al. (2015) and in terms of extreme events in Zollo et al. (2015). In particular, the Representative Concentration Pathway (RCP) 8.5 scenario was applied, based on the IPCC (Intergovernmental Panel on Climate Change) modelling approach to generate greenhouse gas (GHG) concentrations (Meinshausen et al., 2011). Initial and boundary conditions for running RCM simulations with COSMO-CLM were provided by the general circulation model CMCC-CM (Scoccimarro et al., 2011), whose atmospheric component (ECHAM5) has a horizontal resolution of about 85 km. The simulations covered the period from 1971 to 2100; more specifically, the CMIP5 historical experiment (based on historical greenhouse gas concentrations) was used for the period 1976–2005 (Reference Climate scenario - RC), while for the period 2006–2100, a simulation was performed using the IPCC scenario mentioned. The analysis of results was made on RC (1971–2005) and RCP 8.5 divided into three different time periods (2010–2040, 2040–2070 and 2070–2100). Daily reference evapotranspiration (ET<sub>0</sub>) was evaluated according to Hargreaves and Samani, (1985) equation (HS). The reliability of this equation in the study area was perrformed by Fagnano et al., (2001) comparing the HS equation with the Penman–Monteith (PM) equation (Allen et al., 1998).</p> <p>Under the RCP 8.5 scenario the temperature in Destra Sele is expected to increase approximately two degrees celsius respectively every 30 years to 2100 starting from the RC. The differences in temperature between RC and the period 2070–2100 showed an average increase of minimum and maximum temperatures of about 6.2°C (for both min and max). The projected increase of temperatures produces an increase of the expected ET<sub>0</sub>. In particular, during the maize growing season, an average increase of ET<sub>0</sub> of about 18% is expected until 2100.</p> <p>The climate data were provided by the “Regional Models and Geo-Hydrogeological Impacts Division” of the Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), Capua (CE) – Italy, through the support of Dr. Paola Mercogliano and Dr. Edoardo Bucchignani.</p> <p><strong>References</strong></p> <p>Allen, R. G., Pereira, L. S., Raes, D., Smith, M. and W, a B.: Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56, Irrig. Drain., 1–15, doi:10.1016/j.eja.2010.12.001, 1998.</p> <p>Bucchignani, E., Montesarchio, M., Zollo, A. L. and Mercogliano, P.: High-resolution climate simulations with COSMO-CLM over Italy: performance evaluation and climate projections for the 21st century, Int. J. Climatol., 36(2), 735–756, 2015.</p> <p>Fagnano, M., Acutis, M. and Postiglione, L.: Valutazione di un metodo semplificato per il calcolo dell'ET<sub>0</sub> in Campania, Model. di Agric. sostenibile per la pianura meridionale Gest. delle risorse idriche nelle pianure irrigue. Gutenberg, Salerno, ISBN, 88–900475, 2001.</p> <p>Hargreaves, G. H. and Samani, Z. A.: Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1(2), 96–99, 1985.</p> <p>Kroes, J. G., Van Dam, J. C., Bartholomeus, R. P., Groenendijk, P., Heinen, M., Hendriks, R. F. A., Mulder, H. M., Supit, I. and Van Walsum, P. E. V: Theory description and user manual SWAP version 4, http://www.swap.alterra.nl, Wageningen [online] Available from: www.wur.eu/environmental-research (Accessed 24 July 2019), 2017.</p> <p>Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K. and others: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Clim. Change, 109(1–2), 213, 2011.</p> <p>Rockel, B., Will, A. and Hense, A.: The regional climate model COSMO-CLM (CCLM), Meteorol. Zeitschrift, 17(4), 347–348, 2008.</p> <p>Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Fogli, P. G., Manzini, E., Vichi, M., Oddo, P. and Navarra, A.: Effects of Tropical Cyclones on Ocean Heat Transport in a High-Resolution Coupled General Circulation Model, J. Clim., 24(16), 4368–4384, doi:Doi 10.1175/2011jcli4104.1, 2011.</p> <p>Zollo, A. L., Turco, M. and Mercogliano, P.: Assessment of hybrid downscaling techniques for precipitation over the Po river basin, in Engineering Geology for Society and Territory-Volume 1, pp. 193–197, Springer., 2015.</p> <p> </p> <p> </p>
Estimated future global lightning strokes (2010-2100)
<p><strong>Estimated future lightning stroke density for FireMIP/ISIMIP</strong></p> <p>Jed O. Kaplan, Alexander Koch, and Katie Hong-Kiu Lau<br> Department of Earth Sciences, The University of Hong Kong<br> <a href="mailto:jed.kaplan@arve-research.com">jed.kaplan@arve-research.com</a></p> <p><strong>Introduction</strong></p> <p>This dataset contains scenarios of global lightning density (strokes km<sup>-2</sup> day<sup>-1</sup>) for the period 2010-2100. The lightning density estimates are available on an unprojected global half-degree grid with monthly temporal resolution. Each pixel represents the monthly mean of daily lightning stroke density. The estimates of lightning density cover all global land and ocean areas. This dataset may be used in a range of applications, for example, to understand the influence of lightning on wildfire ignitions or atmospheric composition.</p> <p><strong>Methods</strong></p> <p>The estimate of lightning density is based on future climate simulations from an earth system model and an emprical relationship between Convective Available Potential Energy (CAPE) and lightning strokes based on the WWLLN Global Lightning Climatology and timeseries (<a href="https://doi.org/10.5281/zenodo.6007052">WGLC</a>) (Kaplan and Lau 2021, 2022).</p> <p>Future climate simulations performed with the <a href="https://ukesm.ac.uk/cmip6/">UKESM1</a> earth system model for the 6th Coupled Model Intercomparison Project (<a href="https://agupubs.onlinelibrary.wiley.com/doi/toc/10.1002/%28ISSN%291942-2466.UKESM1">CMIP6</a>) provided the following meteorological outputs on pressure levels from the surface to the top of the troposphere required for calculating CAPE:</p> <ul> <li>air temperature (<em>ta</em>, K)</li> <li>air pressure (<em>p</em>, Pa)</li> <li>specific humidity (<em>hus</em>, kg kg-1).</li> </ul> <p>We derived dewpoint temperature (<em>T<sub>d</sub></em>, K) based on <em>ta</em> and <em>hus</em>. CAPE was then estimated using the <code>getcape.F</code> (Version 1.02) code by George Bryan that is part of the NCAR <a href="https://www2.mmm.ucar.edu/people/bryan/cm1/">CM1</a> atmosphere model .</p> <p>After experimenting with climate model output on different timesteps (6-hourly, daily, monthly) we decided to use daily mean values for calculating CAPE as it provided a reasonable balance of accuracy and computational time. The calculated CAPE values were linearly interpolated from the native UKESM grid resolution to a global half-degree grid using the <a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a> software</p> <p>We estimated lightning stroke density based on an emprical relationship between daily CAPE and observed lightning over the period 2015-2020. Lightning observations were provided by the WGLC. The relationship between CAPE and lightning is described by a linear regression on log(CAPE) vs. log(WGLC) for each gridcell and month of the year. Where <10 observations of daily lightning were available over the calibration period, we used global mean regression parameters.</p> <p>Using the relationship described above, we generated an estimate of future lightning. To better maintain the spatial structure of lightning observed at present, we created our dataset using future lightning anomalies added to the present-day lightning climatology. We generated anomalies of the lightning estimate for each month from 2015-2100 (made with the emprical relationship described above) relative to the simulated 2015-2020 multi-year monthly mean (i.e., climatological mean lightning) using simple subtraction. We then added these anomalies using to the 2015-2020 climatological mean lightning from the WGLC. For the period 2010-2014 we used the WGLC, and for 2015-2020 we over-wrote the estimated lightning with observed data from the WGLC.</p> <p>We estimate lighting for four future scenarios using the Shared Socioeconomic Pathways (SSPs; Riahi et al., 2017) SSP126, SSP245, SSP370, and SSP585.</p> <p><strong>Data specification</strong></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) files following the <a href="http://cfconventions.org/">CF conventions</a> and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: 30 arc-minutes (half degree)</li> <li>Temporal extent: 2010-2100</li> <li>Temporal resolution: 1 month (1080 elements)</li> </ul> <p>File naming convention:</p> <pre><code>[model name]-[version]_futurelght_[scenario]_[period]_[method].nc </code></pre> <p>e.g., <code>UKESM1-0-LL_futurelght_ssp370_2010-2100_Aday.nc</code></p> <p>means</p> <ul> <li>UKESM1 version 0-LL,</li> <li>with the SSP370 future scenario,</li> <li>covering the period 2010-2100,</li> <li>using mean daily meteorology (Aday) to calculate CAPE.</li> </ul> <p>NB in this release the SSP126 scenario covers only 2010-2099.</p> <p><strong>Further information</strong></p> <p>For further information about this dataset, please contact Jed Kaplan <a href="mailto:jed.kaplan@arve-research.com">jed.kaplan@arve-research.com</a>.</p> <p><strong>References</strong></p> <p>Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. <em>Earth System Science Data, 14</em>(12), 5665-5670. <a href="https://doi.org/10.5194/essd-14-5665-2022">doi:10.5194/essd-14-5665-2022</a></p> <p>Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. <em>Earth System Science Data, 13</em>(7), 3219-3237. <a href="https://stackedit.io/doi.org/10.5194/essd-13-3219-2021">doi:10.5194/essd-13-3219-2021</a></p> <p>Kaplan, Jed O., & Lau, Katie Hong-Kiu. (2021). The WWLLN Global Lightning Climatology and timeseries (WGLC) (v2022.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6007052">doi:10.5281/zenodo.6007052</a></p> <p>Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., & Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. <em>Global Environmental Change, 42</em>, 153-168. doi:10.1016/j.gloenvcha.2016.05.009</p>
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