Conference PaperPDF Available

GEOGRAPHICAL INFORMATIONAL SYSTEMS AND CLIMATE PROJECTIONS IN THE REPUBLIC OF MOLDOVA

Authors:
  • Institute of Ecology and Geography, Moldova

Abstract and Figures

Presented article contains first attempt to evaluate modification of monthly sums of precipitation in the limits of Moldova under influence of future climate conditions. Initialy, climate projection database from global climate models (GCMs) for four representative concentration pathways (RCPs): 2.6, 4.5, 6.0 and 8.5 have been generated for the territory of the Republic of Moldova using the recent Coupled Model Inter-comparison Project 5 (GMIP5), a coupled Earth System Model HadGEM2-ES. These climate projections are the GCM projections used in the Fifth Assessment IPCC report. The GCM was downscaled and calibrated using the maps for a monthly sum of precipitations generated using the database from climate stations and posts of the State Hydromeorological Service of Moldova for the period 1981-2010. Based on generated maps the monthly sums of precipitation were calculated for two main projected periods 2050 (average of 2040-2060) and 2070 (average of 2060-2080) for all administrative districts of the Republic of Moldova. Analysis of mod-eled data shows that modifications are characteristic for all months: substantial reduction being for July and August and relative increases being for October-May. The results of climate change projections can be utilized for generating bio-climatic variables and evaluation of future water resources essential to define agricultural scenarios in Moldova as affected by climate change.
Content may be subject to copyright.
Proceedings of the 22th Edition of the International Symposium Geographic Information Systems
24-25th of October 2014, Chisinau, Republic of Moldova
72
GEOGRAPHICAL INFORMA-
TIONAL SYSTEMS AND CLIMATE
PROJECTIONS IN THE REPUBLIC
OF MOLDOVA
Jeleapov Ana
1
, Rawat Monica2, Raileanu V.3,
Nedealcov Maria3, Sena D. R.4
1Laboratory of Landscape Ecology, Institute of
Ecology and Geography of Academy of Sci-ences of
Moldova
2 Uttarakhand Open University, Nainital, Ut-
tarakhand, India
3Laboratory of Climatology and Enironmental
Risks, Institute of Ecology and Geography of
Academy of Sciences of Moldova
4Central Soil and Water Conservation, Re-search
and Training Institute, Dehradun, India
Abstract. Presented article contains first attempt
to evaluate modification of monthly sums of precipita-
tion in the limits of Moldova under influence of future
climate conditions. Initialy, climate projection database
from global climate models (GCMs) for four representa-
tive concentration pathways (RCPs): 2.6, 4.5, 6.0 and
8.5 have been generated for the territory of the Repub-
lic of Moldova using the recent Coupled Model Inter-
comparison Project 5 (GMIP5), a coupled Earth System
Model HadGEM2-ES. These climate projections are the
GCM projections used in the Fifth Assessment IPCC
report. The GCM was downscaled and calibrated us-
ing the maps for a monthly sum of precipitations gener-
ated using the database from climate stations and posts
of the State Hydromeorological Service of Moldova for
the period 1981-2010. Based on generated maps the
monthly sums of precipitation were calculated for two
main projected periods 2050 (average of 2040-2060)
and 2070 (average of 2060-2080) for all administrative
districts of the Republic of Moldova. Analysis of mod-
eled data shows that modifications are characteristic for
all months: substantial reduction being for July and
August and relative increases being for October-May.
The results of climate change projections can be utilized
for generating bio-climatic variables and evaluation of
future water resources essential to define agricultural
scenarios in Moldova as affected by climate change.
Keyword: CMIP5, HadGEM2-ES, RCPs, Cli-
mate Change, DIVA-GIS
1 Contact information: Ana Jeleapov, Labo-
ratory of Landscape Ecology, Institute of Ecology
and Geography of Academy of Sciences of Moldo-
va, Academiei str., 1, MD 2028, Chisinau, Republic
of Moldova, e-mail: anajeleapov@gmail.com, tel.
(+373) 68473729
Proceedings of the 22th Edition of the International Symposium Geographic Information Systems
24-25th of October 2014, Chisinau, Republic of Moldova
73
Introduction
The Republic of Moldova is an agricultural coun-
try. Its exposure to climate changes increases the
vulnerability for safe and sustainable development of
agriculture. Climate changes over the past 25 years
have been manifested by sudden increase of fre-
quency and amplitudes of temperature and precipi-
tation extremes which caused tremendous damage
to the national economy. Some forecasts show that
climate change will increase in the future. In this cont-
ext the main goal is to assess the future climate con-
ditions in order to develop action plans for adaptation
and mitigation of climate change. Utilization of global
and regional climate models (GCMS, RCMS) and
development of climate projections, especially recent
projections (GMIP5, HadGEM2-ES) will contribute to
achieve this goal. Present study contains the results
of modeling of monthly sums of precipitation basing
on climate changes projections from 5th IPCC report
for two projected periods: 2050 and 2070.
Material and methods
The precipitations have a dynamic character and
a high variability in space. First attempt for creating
precipitation maps for territory of the Republic of Mol-
dova was made in 2013 through creating the Atlas of
Climatic Resources of the Republic of Moldova [5].
The atlas contains a set of thematic maps reflecting
the spatial distribution of monthly, seasonal, and an-
nual mean air temperature, the average amount of
monthly, seasonal and annual precipitations for a pe-
riod of 30 years (1981-2010). The data recorded from
meteorological stations and posts of State Hidrome-
teorological Service served as starting material. All
maps were developed at scale 1:1500000 in Uni-
versal Transversal Mercator projection (UTM), using
cartographic modeling. Collected data correspond to
meteorological shelter height (2 m).
Cartographic modeling was performed in two
stages. At the first stage the method of multiple
regression with step procedures was used, that al-
lowed highlighting the values that reflect the tem-
perature and rainfall dependence of several local
physical-geographical factors. As indicators of model
validation were used: the test of significance of each
physical- geographical factor taken separately and
that of the entire model, the coefficient of determina-
tion, the standard error of estimation, and the mean
absolute error. At the second stage, the residuals of
regression, which are determined by unknown fac-
tors, were interpolated using a local interpolator. The
results of the interpolations were summed with the
results of the regression model. The interpolators
take into account only the data neighboring with the
interpolated point.
Climate projection database from global climate
models (GCMs) for four representative concentration
pathways (RCPs 2.6, 4,5, 6,0 and 8,5) have been
generated for Moldova using CMIP5 (Coupled Model
Inter-comparison Project 5) centennial simulation
carried out by Met Office Hadley Centre, a Coupled
Earth System Model HadGEM2-ES [1, 3, 4]. These
GCM projections are used in the Fifth Assessment
IPCC (Intergovernmental Panel on Climate Change)
report (AR5). The GCM output was downscaled and
calibrated (bias-corrected), using Atlas of Climatic
Resources of the Republic of Moldova [5].
The 200x200m gridded data for current climate
(1981-2010) and future monthly rainfall data (2050
and 2070) in GIS format were extracted and an
ensemble for the 4 RCPs at near surface resolution
200x200 m for Moldova was prepared in the form of
ready to use climate format according to following
principles:
(1)
where: = is the fraction of the relative change in
monthly precipitation in projected period with respect to
base period projection
= represents monthly precipitation data
derived at a spatial resolution of 1 km X 1 km for the
base period (1950-2000) [2].
= is monthly precipitation data derived at
a spatial resolution of 1 km X 1 km for the projected
period, downscaled global climate model (GCM) data
from CMIP5 (IPPC Fifth Assessment) 2050 (2040-
2060) or 2070 (2060-2080) [2] for different RCPs.
is converted to low resolution iso-C maps
after applying appropriate spatial interpolation
technique using Arc GIS and re-sampled and gridded to
200 m spatial resolution and grid values were
represented as . The projected precipitation
data at 200mX200m resolution is therefore, derived
using the formula
(2)
where,
high resolution monthly precipitation maps
downscaled and calibrated (bias-corrected), using Atlas
of Climatic Resources of the Republic of Moldova
(Nedealcov et al., 2013)
The procedure was uniformly adopted to create
the layers of precipitation for the projected period
2050 and 2070 in all RCPs i.e. RCP 2.5, 4.5, 6.0 and
8.5 (fig. 1, 2, 3). The climate files (.clm files) were
prepared using DIVA-GIS interface.
Results and discusions
Climate change modeling was performed for
sums of precipitation for all months of the year for
two main projected periods 2050 (average of 2040-
2060) and 2070 (average of 2060-2080). Changes
are characteristic for all months of the year. During
the year there can be highlighted both reductions
Proceedings of the 22th Edition of the International Symposium Geographic Information Systems
24-25th of October 2014, Chisinau, Republic of Moldova
74
and increases in monthly sums of precipitation. Sig-
nificant decreases in monthly sums of precipitations
are observed during warm period and especially for
July-August when reductions (for July) are in the lim-
its of -1.16% (RCP 2.6, 2050) and -43% (RCP 8.5,
2070). Precipitation increases are observed during
autumn and spring. In January there are observed
changes in rainfall from -1.6 to + 24%. Figures 1 and
2 represent the grids for July and January for both the
base period (1981-2010) and for 2050 period. Also,
in fig. 4 and tab. 1 the averages for the same periods
as well as 2070 period for all months of the year are
Figure 2. Recent
(1981-2010) and
projection (2050)
precipitation in July
for 4 RCPs
Figure 1. Recent
(1981-2010) and
projection (2050)
precipitation in
January for 4 RCPs
Proceedings of the 22th Edition of the International Symposium Geographic Information Systems
24-25th of October 2014, Chisinau, Republic of Moldova
75
Figure 3. Monthly precipitations for base period (columns) and for 2050 and 2070 (lines)
Table 1. Sums of precipitation for all months of the year for different periods
Months
Base
period
1981-2010
Period 2070 (2060-2080)
RCP 2.6
RCP 4.5
RCP 6.0
RCP 8.5
RCP 2.6
RCP 4.5
RCP 6.0
RCP 8.5
January
31.5
31.0
32.2
27.6
33.6
33.0
32.1
33.0
39.2
February
28.8
33.9
35.5
32.9
28.9
33.5
36.5
32.4
40.1
March
29.7
32.7
35.3
36.1
35.8
34.4
36.1
36.7
38.1
April
40.1
42.2
46.5
41.0
45.9
43.7
46.4
45.7
45.4
May
51.3
59.4
56.9
57.9
55.6
56.8
50.7
57.1
54.8
June
75.1
81.0
76.7
83.8
73.7
84.9
74.1
67.7
60.2
July
69.8
69.0
49.3
53.9
44.7
59.3
51.9
46.5
39.8
August
53.3
36.4
33.5
40.4
30.2
41.5
36.0
32.2
24.0
September
52.0
44.2
46.3
42.4
53.0
52.4
49.4
51.0
33.1
October
35.4
45.7
45.8
43.3
45.0
38.6
43.0
44.8
45.3
November
36.7
39.7
35.8
38.7
42.2
38.8
40.9
41.5
39.8
December
34.9
37.9
32.2
35.9
35.8
39.3
37.7
39.9
37.1
Annual
539
553
526
534
524
556
535
528
497
Figure 4. Comparison between monthly precipitations for base period and for projected periods
Proceedings of the 22th Edition of the International Symposium Geographic Information Systems
24-25th of October 2014, Chisinau, Republic of Moldova
76
presented generally for the territory of the Republic
of Moldova. As seen, changes for the period 2070
compared to 2050 are more obvious.
Based on generated maps the monthly sums of
precipitation were calculated for two main projected
periods 2050 (2040-2060) and 2070 (2060-2080) for
all administrative districts of the Republic of Moldova.
As seen from fig. 3 higher differences of precipitation
are characteristic for northern district in comparison
with southern.
Conclusion
This research represents the first attempt to gen-
erate precipitations maps for all months of the year
basing on climate changes projections from 5th IPCC
report. Utilization of global climate models with low
spatial resolution, regional climate models and re-
cently created maps of monthly precipitation from
the Atlas of Climatic Resources of the Republic of
Moldova [5] allowed developing regional precipita-
tion projections for four representative concentration
pathways for 2050 and 2070 years for the territory of
the Republic of Moldova. The obtained results can
be used to develop scenarios in order to predict the
risks that could affect Moldova’s agriculture as well
as for assessment of water resources and hydrologi-
cal modeling as well as crop simulation models.
References
1. Collins W. J. et al. Development and evalu-
ation of an Earth-system modelHadGEM2. Geosci.
Model Dev., 4, 9971062, 2011;
2. Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G.
Jones and A. Jarvis, 2005. Very high resolution
interpolated climate surfaces for global land areas.
International Journal of Climatology 25: 1965-1978;
3. Jones C. D. et al., The HadGEM2-ES
implementation of CMIP5 centennial simulations.
Geosci. Model Dev., 4, 543-570, 2011;
4. Martin G. M. et al. The HadGEM2 family of Met
Office Unified Model climate configurations. Geosci.
Model Dev., 4, 723757, 2011;Nedealcov M. et al.
Climatic Resources of Republic of Moldova. Atlas.
Chisinau: IP Ştiinţa. 78 p. 2013.
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability.
Article
Full-text available
The scientific understanding of the Earth's climate system, including the central question of how the climate system is likely to respond to human-induced perturbations, is comprehensively captured in GCMs and Earth System Models (ESM). Diagnosing the simulated climate response, and comparing responses across different models, is crucially dependent on transparent assumptions of how the GCM/ESM has been driven - especially because the implementation can involve subjective decisions and may differ between modelling groups performing the same experiment. This paper outlines the climate forcings and setup of the Met Office Hadley Centre ESM, HadGEM2-ES for the CMIP5 set of centennial experiments. We document the prescribed greenhouse gas concentrations, aerosol precursors, stratospheric and tropospheric ozone assumptions, as well as implementation of land-use change and natural forcings for the HadGEM2-ES historical and future experiments following the Representative Concentration Pathways. In addition, we provide details of how HadGEM2-ES ensemble members were initialised from the control run and how the palaeoclimate and AMIP experiments, as well as the "emission-driven" RCP experiments were performed.
Article
Full-text available
We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950-2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledge-based methods and inclusion of additional co-variates, particularly layers obtained through remote sensing.
Climatic Resources of Republic of Moldova
  • M Nedealcov
Nedealcov M. et al. Climatic Resources of Republic of Moldova. Atlas. Chisinau: IP Ştiinţa. 78 p. 2013.