A public-private collaboration initiative for innovative Earth Observation (EO) technologies and methodologies for investigating climate change impacts by means of an inter-disciplinary approach: the OT4CLIMA project

2020 ◽  
Author(s):  
Nicola Pergola ◽  
Carmine Serio ◽  
Francesco Ripullone ◽  
Francesco Marchese ◽  
Giuseppe Naviglio ◽  
...  
Keyword(s):  
Climate Change ◽  
Technological Innovation ◽  
Regional Scale ◽  
Climate Change Impacts ◽  
Earth Observation ◽  
Ministry Of Education ◽  
Natural Processes ◽  
Company Survey ◽  
Industrial Partnership

<p>The OT4CLIMA project, funded by the Italian Ministry of Education, University and Research, within the PON 2014-2020 Industrial Research program, “Aerospace” thematic domain, aims at developing advanced Earth Observation (EO) technologies and methodologies for improving our capability to better understand the effects of Climate Change (CC) and our capability to mitigate them at the regional and sub-regional scale. Both medium-to-long term impacts (e.g. vegetation stress, drought) and extreme events with rapid dynamics (e.g. intense meteorological phenomena, fires) will be investigated, trying a twofold (i.e. interesting both “products” and “processes”) technological innovation: a) through the design and the implementation of advanced sensors to be mounted on multiplatform EO systems; b) through the development of advanced methodologies for EO data analysis, interpretation, integration and fusion.</p><p>Activities will focus on two of the major natural processes strictly related to Climate Change, namely the Carbon and Water Cycles by using an inter-disciplinary approach.</p><p>As an example, the project will make it possible the measurements, with an unprecedented accuracy of atmospheric (e.g. OCS, carbon-sulphide) and surface (e.g. soil moisture) parameters that are crucial in determining the vegetation contribution to the CO2 balance, suggesting at the same time solutions based on the analysis and integration of satellite, airborne and unmanned data, in order to significantly improve the capability of local communities to face the short- and long-term CC-related effects.</p><p>OT4CLIMA benefits from a strong scientific expertise (14 CNR institutes, ASI, INGV, CIRA, 3 Universities), considerable research infrastructures and a wide industrial partnership (including both big national players, i.e. E-Geos and IDS companies and well-established italian SMEs consortia, i.e. CREATEC, CORISTA and SIIT, and a spin-off company, Survey Lab) specifically focused on the technological innovation frontier.</p><p>This contribution would summarize the project main objectives and show some activities so far carried out.</p>

scholarly journals A 1-km global dataset of historical (1979–2017) and future (2020–2100) Köppen-Geiger climate classification and bioclimatic variables

2021 ◽  
Author(s):  
Diyang Cui ◽  
Shunlin Liang ◽  
Dongdong Wang ◽  
Zheng Liu
Keyword(s):  
Climate Change ◽  
Regional Scale ◽  
Climate Change Impacts ◽  
Global Scale ◽  
Climatic Conditions ◽  
Distribution Models ◽  
Climate Classification ◽  
Climate Zones ◽  
Bioclimatic Variables

Abstract. The Köppen-Geiger climate classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. The Köppen-Geiger climate maps currently available are limited by relatively low spatial resolution, poor accuracy, and noncomparable time periods. Comprehensive assessment of climate change impacts requires a more accurate depiction of fine-grained climatic conditions and continuous long-term time coverage. Here, we present a series of improved 1-km Köppen-Geiger climate classification maps for ten historical periods in 1979–2017 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The historical maps are derived from multiple downscaled observational datasets and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. In addition to climate classification maps, we calculate 12 bioclimatic variables at 1-km resolution, providing detailed descriptions of annual averages, seasonality, and stressful conditions of climates. The new maps offer higher classification accuracy and demonstrate the ability to capture recent and future projected changes in spatial distributions of climate zones. On regional and continental scales, the new maps show accurate depictions of topographic features and correspond closely with vegetation distributions. We also provide a heuristic application example to detect long-term global-scale area changes of climate zones. This high-resolution dataset of Köppen-Geiger climate classification and bioclimatic variables can be used in conjunction with species distribution models to promote biodiversity conservation and to analyze and identify recent and future interannual or interdecadal changes in climate zones on a global or regional scale. The dataset referred to as KGClim, is publicly available at http://doi.org/10.5281/zenodo.4546140 for historical climate and http://doi.org/10.5281/zenodo.4542076 for future climate.

On Climate Change Risks of Long-Term Socio-Economic Development in Russia

2019 ◽  
pp. 79-95
Author(s):  
N.E. Terentiev
Keyword(s):  
Climate Change ◽  
Economic Development ◽  
Technological Innovation ◽  
Global Climate Change ◽  
Global Climate ◽  
Climate Risk ◽  
Climate Change Risks ◽  
Socio Economic Development ◽  
Management Policies

Based on the latest data, paper investigates the dynamics of global climate change and its impact on economic growth in the long-term. The notion of climate risk is considered. The main directions of climate risk management policies are analyzed aimed, first, at reducing anthropogenic greenhouse gas emissions through technological innovation and structural economic shifts; secondly, at adaptation of population, territories and economic complexes to the irreparable effects of climate change. The problem of taking into account the phenomenon of climate change in the state economic policy is put in the context of the most urgent tasks of intensification of long-term socio-economic development and parrying strategic challenges to the development of Russia.

scholarly journals Epidemic malaria and warmer temperatures in recent decades in an East African highland

2010 ◽  
Vol 278 (1712) ◽  
pp. 1661-1669 ◽  
Author(s):  
David Alonso ◽  
Menno J. Bouma ◽  
Mercedes Pascual
Keyword(s):  
Climate Change ◽  
East Africa ◽  
Nonlinear Response ◽  
Climate Change Impacts ◽  
Human Model ◽  
Recent Past ◽  
East African ◽  
Highly Nonlinear ◽  
The Impact

Climate change impacts on malaria are typically assessed with scenarios for the long-term future. Here we focus instead on the recent past (1970–2003) to address whether warmer temperatures have already increased the incidence of malaria in a highland region of East Africa. Our analyses rely on a new coupled mosquito–human model of malaria, which we use to compare projected disease levels with and without the observed temperature trend. Predicted malaria cases exhibit a highly nonlinear response to warming, with a significant increase from the 1970s to the 1990s, although typical epidemic sizes are below those observed. These findings suggest that climate change has already played an important role in the exacerbation of malaria in this region. As the observed changes in malaria are even larger than those predicted by our model, other factors previously suggested to explain all of the increase in malaria may be enhancing the impact of climate change.

scholarly journals Satellites, Self-reports, and Submersion: Exposure to Floods in Bangladesh

2015 ◽  
Vol 105 (5) ◽  
pp. 232-236 ◽  
Author(s):  
Raymond Guiteras ◽  
Amir Jina ◽  
A. Mushfiq Mobarak
Keyword(s):  
Climate Change ◽  
Economic Activity ◽  
Climate Change Impacts ◽  
Objective Data ◽  
Short Term ◽  
Temperature And Precipitation ◽  
Flood Exposure ◽  
Self Reports ◽  
Term Variation

A burgeoning “Climate-Economy” literature has uncovered many effects of changes in temperature and precipitation on economic activity, but has made considerably less progress in modeling the effects of other associated phenomena, like natural disasters. We develop new, objective data on floods, focusing on Bangladesh. We show that rainfall and self-reported exposure are weak proxies for true flood exposure. These data allow us to study adaptation, giving accurate measures of both long-term averages and short term variation in exposure. This is important in studying climate change impacts, as people will not only experience new exposures, but also experience them differently.

Assessment of alpine summit flora in Kashmir Himalaya and its implications for long-term monitoring of climate change impacts

2020 ◽  
Vol 17 (8) ◽  
pp. 1974-1988
Author(s):  
Maroof Hamid ◽  
Anzar Ahmad Khuroo ◽  
Akhtar Hussain Malik ◽  
Rameez Ahmad ◽  
Chandra Prakash Singh
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Kashmir Himalaya ◽  
Long Term Monitoring ◽  
Term Monitoring

Long-term trends in ocean chlorophyll: update from a Bayesian hierarchical space-time model

2020 ◽  
Author(s):  
Claudie Beaulieu ◽  
Matthew Hammond ◽  
Stephanie Henson ◽  
Sujit Sahu
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Climate Change Impacts ◽  
Space Time ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Color Sensors ◽  
Long Term Trends ◽  
Space Time Model

<p>Assessing ongoing changes in marine primary productivity is essential to determine the impacts of climate change on marine ecosystems and fisheries. Satellite ocean color sensors provide detailed coverage of ocean chlorophyll in space and time, now with a combined record length of just over 20 years. Detecting climate change impacts is hindered by the shortness of the record and the long timescale of memory within the ocean such that even the sign of change in ocean chlorophyll is still inconclusive from time-series analysis of satellite data. Here we use a Bayesian hierarchical space-time model to estimate long-term trends in ocean chlorophyll. The main advantage of this approach comes from the principle of ”borrowing strength” from neighboring grid cells in a given region to improve overall detection. We use coupled model simulations from the CMIP5 experiment to form priors to provide a “first guess” on observational trend estimates and their uncertainty that we then update using satellite observations. We compare the results with estimates obtained with the commonly used vague prior, reflecting the case where no independent knowledge is available.  A global average net positive chlorophyll trend is found, with stronger regional trends that are typically positive in high and mid latitudes, and negative at low latitudes outside the Atlantic. The Bayesian hierarchical model used here provides a framework for integrating different sources of data for detecting trends and estimating their uncertainty in studies of global change.</p>

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