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Characterizing Global Climate Change by means of Köppen Climate Classification



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Christoph Beck
, Jürgen Grieser
, Markus Kottek
, Franz Rubel
and Bruno Rudolf
Global Precipitation Climatology Centre, Deutscher Wetterdienst, Kaiserleistr. 44, 63067 Offenbach, Germany
Biometeorology Group, University of Veterinary Medicine, Veterinärplatz 1, 1210 Vienna, Austria
Global climate classifications were originally constructed in order to designate the
manifold existing local climates to an adequate number of climate types and to
determine the spatial distribution of these types on the basis of climatic data for a
reference period. Thus, climate classifications are introduced in order to reflect the
mean spatial climate characteristics.
However, the underlying climate variables are subject to temporal variations and so
are the results of climate classifications. Therefore climate classifications may not
only be used to determine the mean state of the climate. They can also be utilized to
analyse global and regional scale climate variations by applying them to varying time
periods. Spatio-temporal variations of climate types resulting from effective climate
classifications do not only reflect modifications of climatic parameters. By definition
they are closely linked to different environmental conditions (e.g. vegetation) and
therefore they may moreover be used for investigating the potential impact of past,
present and projected future climate change on environmental systems.
Varying aspects of global and regional climate change have been investigated on the
basis of the well-known climate classification according to Köppen (e.g. Köppen
1936, Geiger 1961) by several authors. Fraedrich et al. (2001) analysed the shifts of
modified Köppen climate types on a global and continental scale during the 20
century, Grieser et al. (2006) compared the results of a Köppen classification applied
to gridded data from the periods 1951 – 1975 and 1976 - 2000. Focusing on a more
regional perspective Suckling and Mitchell (2000) investigated variations of the
boundary between the Köppen C and D climates in the Central United States and
Wang and Overland (2004) analysed arctic climate change during the 20
century on
the basis of a modified Köppen classification. Triantafyllou and Tsonis (1994)
assessed the sensitivity of the Köppen classes to long-term climate change on the
basis of long station time series. By applying the Köppen classification to the output
of general circulation models (hereafter GCM) Guetter and Kutzbach (1990)
estimated the main characteristics of glacial and interglacial climates, Kalvova et al.
(2003) compared the results of Köppen classifications applied to several 20
observational data sets and varying GCM outputs and Lohmann et al. (1993) used
the Köppen classification to validate GCM control runs and as well to analyse
scenario runs concerning spatio-temporal variations of climate types.
In this contribution the spatio-temporal climate variations during the second half of
the 20
century are investigated on global and continental scale by applying the
Köppen climate classification to monthly precipitation and temperature data available
from two most recently constructed globally gridded data sets (Mitchell and Jones
2005, Beck et al. 2005).
Monthly temperature and precipitation data for the period 1951 - 2000
Monthly mean temperatures and monthly precipitation sums have been taken from
the CRU TS 2.1 data set (Mitchell and Jones, 2005) provided by the Climatic
Research Unit (CRU, University of East Anglia, Norwich, GB) and the VASClimO
v1.1 data set (Beck et al. 2005) available from the Global Precipitation Climatology
Centre (GPCC, Deutscher Wetterdienst, Offenbach, Germany) respectively. Both
data sets cover the analysed period from 1951 – 2000 and provide data for the global
land areas on a 0.5° by 0.5° lat./lon. grid. Due to insufficient station densities
Greenland and Antarctica are not included within the VASClimO precipitation data set
(Beck et al. 2005) and therefore these regions are also excluded from the analyses
presented here.
The same data base has also been used for applications of the Köppen climate
classification on the basis of state of the art data sets most recently performed by
Kottek et al. (2006) and Grieser et al. (2006).
The Köppen climate classification
Most global climate classifications are so called effective classifications which use
threshold values (mostly of monthly temperature and precipitation data) based on
environmental characteristics (e.g. vegetation) for the definition of boundaries
separating different climate types. Probably the most prominent effective
classification is the classification scheme according to Köppen (1936) that is based
on the characteristics of the mean annual cycle of temperature and precipitation.
Threshold values utilizing these climatic parameters have been defined in order to
designate climate types that reflect major environmental characteristics (i.e. the 10°
C isotherm of the warmest month in the year as indicator for tree growth).
The resulting five major climate types according to Köppen (1936) designated by
capital letters are:
Tropical rain climates (A) – where the mean temperature of the coldest month
exceeds +18.0°C.
Arid climates (B) are defined as follows on the basis of the average annual
precipitation sum R (cm) and the annual mean temperature T (°C):
R < 2T + 28 (where summer rain is dominating)
R < 2T +14 (where no pronounced annual cycle is observed)
R < 2T (where winter rain is dominating)
Temperate rain climates (C) – where the mean temperature of the coldest month
is between –3.0°C and +18.0°C.
Boreal forest and snow climates (D) are characterized by a mean temperature
of the warmest month exceeding 10.0°C and a mean temperature of the coldest
month below –3.0°C.
Cold snow climates (E) are defined by a mean temperature of the warmest
month below 10.0°C.
A further subdivision of these five main types concerning further parameters
representing temperature and precipitation conditions leads to subtypes designated
by two resp. three letter codes (e.g. Cf where f indicates humid conditions
throughout the year, and Cfa – where a indicates hot summer conditions). In this
paper the presentation of results is restricted to the five main Köppen types described
A more comprehensive overview on the Köppen classification can be found for
example in Kraus (2001).
Analysing spatio-temporal variations of Köppen climates
In order to investigate the spatiotemporal variations of Köppen climate types during
the 1951 – 2000 period the classification scheme has been applied to monthly mean
values determined for sliding 15-year intervals, as has been proposed by Fraedrich et
al. (2001). For each of these 36 intervals (indicated by the respective central year)
the areas that are designated to each Köppen climate type have been calculated and
continuous time series of the varying relative area (percentage of the global resp.
continental land area excluding Greenland and Antarctica) occupied by the certain
types have been obtained.
For each 15-year interval its similarity to the classification results obtained on the
basis of long term mean values from the whole 50-year period from 1951 – 2000 is
estimated in terms of the percentage of the global land area assigned to the same
climate type.
The spatial distribution of the differences between classification results obtained for
the 50 year period and the one 15-year interval showing greatest deviations were
determined and the respective transfer matrix summarizing the respective
redistributions between the five main climate types was calculated.
As a change from one climate type to another between two periods may be caused
by rather small differences concerning the relevant climatic parameter (e.g. annual
precipitation sum) for those gridcells assigned to different climate types in the two
periods it is furthermore investigated in how far these differences reflect a statistical
significant climate change concerning those parameters that are relevant for the
respective redistribution by applying the non-parametric U-test according to Mann-
Whitney (e. g. Bahrenberg et al. 1990). Concerning for example a change from
climate type A to climate type C it is tested if the temperature of the coldest month
shows statistical significant differences (at the 90% or 95% level) between the two
The Köppen classification restricted to the five main types based on monthly mean
values for the period 1951 – 2000 is shown in Fig. 1. The temporal variations of the
relative area occupied by each of the five main Köppen climates estimated for sliding
15-year intervals during the 1951 2000 period are depicted in Fig. 2 for the whole
globe and in Fig. 3 for the continental scale regions (marked by grey rectangles in
Fig. 1).
Concerning the long-term variations of the global land area occupied by the five main
Köppen types (see Fig. 2) the most striking features are a distinct reduction of the
areas assigned to the polar E and boreal D climates as well as a concomitant
expansion of dry B climates. Tropical A and temperate C climates on the other hand,
although showing marked decadal scale variations exhibit no such distinct long term
Figure 1: Spatial distribution of the five main Köppen climate types determined for the period
1951 – 2000. Rectangles indicate the continental sub-regions for which selected results are
As depicted in Fig. 3 the trends in land fractions occupied by Köppen classes for
each of the continental-scale regions may differ considerable from the global trends.
North America
Most striking for North America appear distinct reductions of polar E and as well dry
B climates. Simultaneously the area occupied by the temperate C and boreal D
climate types increases.
In Asia polar E climates exhibit a reduction in coverage as well. But in contrast to
North America dry B climates expand whereas temperate C and boreal D climates
feature downward trends in coverage.
An expansion of dry B climates can also be stated for Europe but most important
appears a sharp increase / decrease of the area occupied by temperate C / boreal D
climates affecting up to 10% of the European land area.
South America
In South America positive trends occur for temperate C and tropical A climates
whereas dry B climates undergo a long term reduction in coverage. Additionally a
slightly decreasing trend in the coverage of cold E climate can be detected.
The expansion of dry B climates that can also be seen on the global scale and in
Europe and Asia appears most striking in Africa where its increase in percentage
since 1951 reaches around 5%. This expansion of dry B climates is compensated by
concomitant reductions of the area covered by tropical A and especially temperate C
Australia shows marked inversely coupled decadal scale variations of tropical climate
types A (and partly temperate C climates) on the one hand and dry B climates on the
other hand. However no clear-cut long-term trends can be deduced.
Figure 2: Time series of the relative area (percentage of the global land area excluding
Greenland and Antarctica) occupied by the main Köppen types.
North America
Figure 3:
Time series of the relative area (percentage of the resp. continental land area
excluding Greenland and Antarctica) occupied by the main Köppen types. For the continental
scale subregions indicated in Fig. 1.
Figure 3: cont.
With respect to Fig. 2 it may be concluded that the last decades of the 20
represent exceptional climate conditions in terms of the distribution of Köppen climate
types. For example the area assigned to dry B climates reaches its maximum in the
1986 – 2000 period while boreal D and cold E climates exhibit global minima
concerning their coverage on the global scale. For each 15-year interval Fig. 4 shows
the relative area assigned to the same climate type as in the 1951 2000 period. It
becomes clear that the 1986 2000 period indeed shows the most distinct
differences to classification results obtained for the whole period. This is consistent
with the general knowledge that the 1986 – 2000 interval represents a period of
accentuated global warming as it comprises 13 of the 15 warmest years within the
period 1951 – 2000. Thus, it may be considered as a recent representation of a
projected globally warmer future climate documented by e.g. IPCC (2001).
Figure 4: Time series of the relative fraction of global land area denoted to the same Köppen
climate type as in the period 1951 – 2000. Sliding 15-year intervals have been applied.
Against this background it is desirable to take a closer look at the differences
between the results of Köppen classifications applied to the 1986 – 2000 interval and
the whole analysis period from 1951 – 2000. The respective redistributions between
the five main Köppen climate types are summarized in the transfer diagram given in
Fig. 5. The resulting differences between the two periods concerning the spatial
distribution of climate types are depicted in Fig. 6.
96.7% of the global land area are assigned to the same main Köppen climate in the
two periods under consideration. For the remaining 3.3% land fraction redistributions
between the five classes occur between 1951 – 2000 and 1986 – 2000. According to
Fig. 5 major transfers between the five main climates took place from climate type C
to A, from climate types A, C and D to B, from D to C and from E to D. As result the
area occupied by the dry climate type B increases towards the end of the 20th
century while the boreal and cold climates D and especially E exhibit reductions in
coverage in the most recent 15-year interval. The tropical A climate and the
temperate C climate feature only minor differences – slight expansions during 1986 –
2000 - between the two periods.
Fig. 5: Transfers between the five main Köppen climate types from the period 1951 - 2000 to the
most recent 15-year interval from 1986 - 2000 in terms of percentages of the global land area
(excluding Greenland and Antarctica). The four rectangles and the oval represent the five main
Köppen climate types characterised by temperature and precipitation conditions respectively.
Numbers above/below the one letter Köppen codes indicate the relative area occupied by each
type during the 1951 – 2000/1986 2000 period respectively. Blue/red Numbers to the right
represent the associated area gains/losses. Arrows indicate redistributions between climate
types exceeding 0.01%.
Only around 92% of the global land area appear as stable climates when comparing
the two selected periods on the basis of the two letter coded 14 Köppen subtypes.
Except for the dry B climates these 14 classes are differentiated concerning
precipitation characteristics. Thus it can be stated that considerable redistributions
occur within the five main climate types, mainly due to precipitation variations (not
shown here). In summary the differences between the period 1951 2000 and the
interval 1986 - 2000 regarding the extension of the five main Köppen climates as
displayed in Fig. 5 support a global warming tendency towards the end of the 20
century. This warming trend is reflected particularly by a reduction of the area
occupied by boreal D and cold E climate types as well as by a decrease in
precipitation implying an expansion of dry B climates.
As can be seen from Fig. 6 the reduction of the area designated to the boreal and
cold climate types D and E appears most pronounced in high latitudes of North
America and in the north-eastern part of Asia where cold E climates are replaced by
boreal D climates, which in turn are substituted over large areas by temperate C
climates in Eastern Europe and the USA. Expansions of dry B climates on the other
hand occur most strikingly in southern and sub-Saharian Africa and the eastern and
central parts of Asia. Concerning tropical A climates - as temperate C climates
exhibiting slight increases in coverage on the global scale (Fig. 5) – the spatially most
coherent changes can be found in southern Africa where tropical A climates gain
area mainly on the expense of temperate C climates.
Figure 6: Köppen climate types for the period 1986 - 2000 for gridcells with different Köppen
climates within the periods 1951 – 2000 and 1986 2000, respectively. Grey and black dots
indicate gridcells for which a difference concerning the respective relevant climate parameter
between the two periods can be determined on the 90% or 95% level of significance,
With respect to the statistical significance of differences of the parameters relevant
for classification results between the two periods (as indicated by grey and black dots
for exceedance of the 90% and 95% level of significance respectively in Fig. 6) it
turns out that for around 1.63% of the global land areas a change from one climate
type to another is accompanied by a statistical significant shift in the mean of the
respective relevant climatic parameter at least at the 90% level of significance. Thus
for about 50% of the area for which changes are found these changes can
significantly be attributed to the main variable. Statistical significant differences over
large areas occur especially
in Africa where temperate C climates are substituted by tropical A climates,
in Africa and eastern Asia where dry B climates replace tropical A and temperate
C climates or boreal D climates respectively, and
in the sub-polar regions of North America and north-eastern Asia where boreal D
climates increased their coverage at the expense of cold E climates.
Also climatic differences linked to the distinct expansion of temperate C climates in
eastern Europe and the USA frequently reach statistical significance although most
often merely at the 90% level.
Regarding the expansion and partly also reduction of dry B climates it has to be
mentioned that there are regional differences concerning the climatic parameter to
which the detected redistributions can be attributed. Especially in the western and
central Sahel regions and throughout Australia but also in the USA redistributions
affecting dry B climates seem to be rather connected with temperature variations
than with precipitation changes.
The application of the global climate classification according to Köppen (1936) to
globally gridded climate data for sliding 15-year intervals in the period 1951 2000
yields an overview of the spatio-temporal variations of main climatic types in a global
and when applied to regional data subsets in a continental perspective.
On the global scale the main temporal variations concerning the area occupied by
the five main Köppen climate types are characterized on the one hand by long-term
declining trends of the area designated to the climate types representing boreal and
cold climates (D and E) and on the other hand by increasing coverages of the dry B
climates. Although tropical A and temperate C climates show marked decadal to
multi-decadal scale variations - including a decline of A climates until the mid 1980s
and a distinct increase of C climates since the 2
half of the 1960s – no such clear
cut tendencies lasting over the whole 1951 2000 period as detected for the above
mentioned types become obvious for these two types. However respective variations
on the continental scale show partly distinctly differing behaviour.
The comparison of classification results achieved for individual 15-year intervals with
the spatial distribution of climate classes obtained on the basis of the whole 1951
2000 period reveals that the most recent sub-period from 1986 – 2000 shows
greatest deviations to the long-term mean climate in terms of area fractions of
different climate types.
The redistributions between the five main Köppen climate types that occurred
between the 1951 2000 period and the 1986 2000 interval affect 3.3% of the
global land area. But only for about the half of this area differing classification results
can be attributed to statistical significant differences of the respective relevant
climatic parameter between the two periods. Transfers between climate types result
in a most recent increase of the dry B climate and concurrent reductions of boreal D
and cold E climates whereas for tropical A and temperate C climates only minor
expansions become visible.
The above findings concerning spatio-temporal variations of the five main Köppen
climates during the 2
half of the 20
century are on a global scale generally in line
with the results obtained by several authors who applied the Köppen classification or
modifications of it to observed 20
century climate data (e.g. Fraedrich et al. 2001,
Kalvova 2003, Grieser et al. 2006) and with respect to more regional scale variations
are furthermore supported for example by Wang and Overland (2004) and Serreze et
al. (2000) concerning the reduction of cold E climates and by Gonzalez (2001)
concerning the expansion of dry B climates in Africa.
As 13 out of the 15 globally warmest years within the 1951 2000 period occured
between 1986 and 2000 this most recent period does not only comprise the effects of
the global warming observed in the 20th century but it may also be considered as an
analogue for a globally warmer climate as it is projected for the future (e.g. IPCC
2001). Thus it may be concluded that the shifts of the main climate types detected for
the 1986 – 2000 interval will persist or may even be amplified under most likely future
conditions of further global warming. This conclusion is supported by the results of
applications of the Köppen climate classification performed on the basis of model
output of coupled and uncoupled GCMs (e.g. Lohmann et al. 1993, Kalvova et al.
2003) indicating an expansion of tropical A and dry B climates and a reduction of
boreal D and cold E climates under the assumption of further greenhouse gas
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... Statistically speaking it's coldest month averages above À3 to 0°C, with at least one month's average temperature reaching above 22°C, and at least four months temperature averaging above 10°C. With winter's wettest month producing at least three times as much precipitation as the driest month of summer [53]. ...
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Today, Building Energy Models (BEM) have become essential in regulatory compliance calculations, the correct assessment of it’s Air Conditioning (AC) systems is critical for the reduction of the performance gap between BEMs and reality and increase the accuracy of evaluating buildings energy performance and it’s systems efficiency. Given that multi-split Variable Refrigerant Flow (VRF) systems have grown in the market in recent years becoming a particular trending solution to achieve building indoor comfort; the present paper focus on technical issues when modelling such VRF systems inside EnergyPlus, a white-box simulation environment, especially regarding the effects weather conditions have on the behaviour of VRF systems and it’s correlation with the AC system performance curves. The study performs an empirical validation of an optimization-based calibration methodology assessing multiple levels: average interior temperature of the different building spaces and electric energy consumption from VRF outdoor unit. It is performed using fifteen minute time-step seasonal data obtained from a fully operational building located in a typical Mediterranean climate (Greece), adjusting the parameter and curve values of the VRF system using a genetic NGSA-II algorithm (Jeplus software) for both summer and winter conditions. The generated BEM captures the building’s hourly performance for summer conditions using 1717 hours to fit into international standards. Complying with the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guidelines 14-2002 for hourly energy consumption, reaching an NMBE ⩽±10% ,Cv(RMSE) ⩽30% and R2 ⩾75% while keeping indoor temperatures on every room with a RMSE ⩽1 °C. The resulting BEM proved stable during the 2077 hours of it’s summer evaluation period, fitting into the new unseen weather and building operation conditions of 2020 which can be considered a step forward in the area of calibrating white box models. While for winter conditions the study demonstrates the value of the calibration methodology while presenting the importance of weather influence on VRF systems. Using a total of 802 hours the applied technology greatly improves the results from the baseline model, reaching a partially calibrated BEM model for winter. Which reinforces the fact that regardless of how good a baseline model is, building operating conditions and weather may will always generate a design/performance gap and therefore the calibration of a BEM is unavoidable.
Coverage, resolution, and accuracy in the spatial and temporal estimates of remotely sensed precipitation from space satellites, along with the number of instruments deployed to deliver these observations, are increasing. Of key interest in this study is the unsurpassed opportunity offered by their broad and continuous coverage to complement sparse, but more accurate, in situ rain gauge measurements for building climate resilience at the local, regional, and global scales. For many parts of the globe, this opportunity remains untapped. Australia is no exception and provides a unique challenge, given the small fraction of the continent that has rain gauges, the highly diverse climate due to its large size, and the apparent worsening of extreme weather events in both frequency and intensity. Notwithstanding this great impetus, a continent-wide record of multi-satellite-gauge fused precipitation data for Australia remains lacking. This missing data asset is a prerequisite for understanding the emerging spatiotemporal dynamics of precipitation, without which reliable forecasts would be difficult if not impossible. This study seeks to address this need. Here, we develop a method which can synergistically fuse precipitation data from different sources. More specifically, the aim of this study is to develop Precipitation Profiler-Observation Fusion and Estimation (PPrOFusE), a tool to deliver high-quality gauge and multi-satellite fused precipitation data. We test and apply this tool for Australia, but it is by no means limited in scope to this region. By design, PPrOFusE has a built-in capability to assess the strengths and weaknesses of each platform. In this case study, we fused data for a period of 22 years (2000–2022) using the rain gauge network data from the Australian Bureau of Meteorology (BOM) together with satellite data from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Center Morphing technique (CMORPH). Our proposed precipitation data fusion method consists of two steps. Step 1, the relationship among the three sources of data is modeled by multiple linear regression at each rain gauge location, returning the least squares estimates for the associated regression coefficient vector. Step 2, such regression coefficient vector estimates for all rain gauge locations are fitted by a spatial autoregression model, whereafter the multiple linear regression coefficient vectors for those locations void of rain gauges are predicted by spatial interpolation. Key findings are twofold. First, CMORPH is more accurate for most regions of Australia than GSMaP. Second, a clustering analysis of the fused precipitation over the last 20 years suggests two key trends on Australia’s changing climate, relative to BOM’s six major climate zones, from the previous century: (a) increased spatial variability to the north, consistent with meteorological expectations, amid a southwards expansion of the wet summer dominant zones across the continent; (b) the edge of the arid region shifts southwards and pushes out Mediterranean climate and winter dominant rainfall zones across southern Australia.
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Wetlands are among the most important ecosystems in the world in terms of endemic biodiversity, carbon storage and hydrological process. Veredas wetlands are distributed across the Brazilian savanna (i.e. Cerrado biome) and are permanently protected areas. Veredas wetlands have a hydromorphic soil, providing water to the main rivers of central Brazil and allowing the occurrence of several endemic species of plants and animals. Although recent studies on biotic and abiotic characteristics have been conducted in several areas of Veredas, the studies are local and there is a lack of information about large-scale patterns. Here we used remote sensing data to explore the role of climate, soil, topography and surrounding matrix explaining Veredas occurrence in the Triângulo Mineiro and Alto Paranaíba (TMAP), a mesoregion of the State of Minas Gerais, Southeastern Brazil. Veredas were more frequent in the western region of TMAP, in areas with lower altitudes, temperature and precipitation seasonality, soil cation exchange capacity, silt and sand content, and slope. Moreover, farming was the most frequent land use in areas surrounding Veredas. Veredas are associated with recharging of the water table and water flow that maintains rivers in the Upper Paraná River water basin. We trust the present assessment will be of help for the development of conservation strategies and biodiversity studies. Graphical abstract Research questions, data processing, statistical analysis and illustration of the outputs generated.
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Wetlands are the largest natural source of methane (CH4); however, the contribution of subtropical wetlands to global CH4 budgets is still unclear due to difficulties in accurately quantifying CH4 emissions from these complex ecosystems. Both direct (water management strategies) and indirect (altered weather patterns associated with climate change) anthropogenic influences are also leading to greater uncertainties in our ability to determine changes in CH4 emissions from these ecosystems. This study compares CH4 fluxes from two freshwater marshes with different hydroperiods (short versus long) in the Florida Everglades to examine temporal patterns and biophysical drivers of CH4 fluxes. Both sites showed similar seasonal patterns across years with higher CH4 release during wet seasons versus dry seasons. The long hydroperiod site showed stronger seasonal patterns and overall, emitted more CH4 than the short hydroperiod site; however, no distinctive diurnal patterns were observed. We found that air temperature was a significant positive driver of CH4 fluxes for both sites regardless of season. In addition, gross ecosystem exchange was a significant negative predictor of CH4 emissions in the dry season at the long hydroperiod site. CH4 fluxes were impacted by water level and its changes over site and season, and time scales, which are influenced by rainfall and water management practices. Thus with increasing water distribution associated the Comprehensive Everglades Restoration Plan we expect increases in CH4 emissions, and when couple with increased with projected higher temperatures in the region, these increases may be enhanced, leading to greater radiative forcing.
Although intensified work on the volcaniclastic-rich sediments of the fossil-bearing Mussentuchit Member (uppermost Cedar Mountain Formation, Utah) has provided a refined chronostratigraphic framework, paleoenvironmental interpretations remain cryptic. To resolve this, we performed facies analysis and architectural reconstruction on exposed Mussentuchit Member outcrops south of Emery, central Utah, USA. Contrary to previous interpretations (fluvial, lacustrine), we identified a broad suite of facies that indicate that deposition occurred on the landward part of a paralic depocenter, influenced by both distal alluvial and proximal coastal systems. We conclude that the Mussentuchit Member was a sink for suspension-settling fines with most undergoing pedogenic alteration, analogous to the modern coastal plain of French Guiana (Wang et al. 2002; Anthony et al. 2010, 2014). However, this landward paralic depocenter was not uniform through time. Sedimentological evidence indicates landscape modification was ongoing, influenced by an altered base-level (high groundwater table, long residency of water in sediments, shifts in paleosol types, heavier to lighter δ18O, and distinct shifts in relative humidity (ε); common in coastal settings). If the above data is coupled with recent age data, we interpret that the Mussentuchit Member correlates to the S.B. 4 Greenhorn Regression (Thatcher Limestone) of the adjacent Western Interior Seaway to the east. As a landward paralic depocenter, the Mussentuchit would have been sensitive to base-level conditions in response to ongoing tectonic processes pushing the foredeep east, and lower paleo-CO2 levels coupled with a minor global sea-level fall (brief glacial phase) just before to the Cenomanian–Turonian Thermal Maximum. Altogether, our results not only strengthen linkages in the central Western Interior Seaway, but simultaneously results in novel linkages to near-coeval paralic depocenters across mid-Cenomanian North America.
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Abstract The climatic zones of Mato Grosso do Sul (MS) were defined based on the mathematical methodology of cluster analysis (CA). Data from 77 climatic seasons of average annual temperatures (maximum and minimum) and total annual precipitation data from 1978 to 2013 were used, and hierarchical (Ward) and partitional or non-hierarchical (k-means) CA algorithms were chosen, as two of the most used approaches, to carry out the regionalization. The optimum number of clusters in which the data can be grouped was determined by the statistical methods of elbow, silhouette and gap. The stability of the clusters is also tested by statistical approaches and four homogeneous groups were found, as in conventional climatic zones, but with considerable border differences. Pearson's correlation coefficient (r) between the series in each cluster helps to understand the dynamics of these clusters. The hierarchical cluster analysis and the elbow method for the optimal number of clusters was the most appropriate and satisfactory and was able to train and validate homogeneous regions of climate in the state of Mato Grosso do Sul. The efficient application of these methodologies is confirmed by the delimitation of four distinct clusters (homogeneous regions of climate), consistent with recorded heights and temperatures (maximum and minimum) and geographical characteristics as topography, in the state of Mato Grosso do Sul.
The increasing greenhouse gas emissions and its consequences, as climate change and global warming have many subsequences for the earth. One of these consequences, particularly for areas of the world that is located in the warm and arid belts, is the increase in arid and semi-arid climate-covered areas. In this study, the Koppen climate classification of Iran in 1975 is compared with the CSIR model outputs classification for the years 2030, 2050, 2080, and 2100 under the two scenarios A1B and A2 which can be found in the Fourth Assessment Report of the IPCC. The Koppen climate classification method categorizes climatic zones based on temperature, precipitation and vegetation. The Koppen method is one of the most widely used climatic classification methods. The results of this study have revealed that in 1975, 82.3% of the country was covered by subgroup B climates, 5.9% was covered by subgroup C and 11.8% was covered by subgroup D climates. However, under both scenarios, the extent of subgroup B climates will increase in the future; so that in 2030, the extent of these areas will reach more than 87%, in 2050 to more than 90%, in 2080 to more than 93% and in 2100 to more than 94%. Generally, the results show a gradual increase in areas covered by warm and arid climates and a decrease in the extent of cold and temperate climates in Iran over the coming years by 2100.
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Ecological impacts of the recent warming trend in the Arctic are already noted as changes in tree line and a decrease in tundra area with the replacement of ground cover by shrubs in northern Alaska and several locations in northern Eurasia. The potential impact of vegetation changes to feedbacks on the atmospheric climate system is substantial because of the large land area impacted and the multi-year persistence of the vegetation cover. Satellite NDVI estimates beginning in 1981 and the Kppen climate classification, which relates surface types to monthly mean air temperatures from 1901 onward, track these changes on an Arctic-wide basis. Temperature fields from the NCEP/NCAR reanalysis and CRU analysis serve as proxy for vegetation cover over the century. A downward trend in the coverage of tundra group for the first 40 yr of the twentieth century was followed by two increases during 1940s and early 1960s, and then a rapid decrease in the last 20 yr. The decrease of tundra group in the 1920–40 period was localized, mostly over Scandinavia; whereas the decrease since 1990 is primarily pan-Arctic, but largest in NW Canada, and eastern and coastal Siberia. The decrease in inferred tundra coverage from 1980 to 2000 was 1.4 106 km2, or about a 20% reduction in tundra area based on the CRU analyses. This rate of decrease is confirmed by the NDVI data. These tundra group changes in the last 20 yr are accompanied by increase in the area of both the boreal and temperate groups. During the tundra group decrease in the first half of the century boreal group area also decreased while temperate group area increased. The calculated minimum coverage of tundra group from both the Kppen classification and NDVI indicates that the impact of warming on the spatial coverage of the tundra group in the 1990s is the strongest in the century, and will have multi-decadal consequences for the Arctic.
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Studies from a variety of disciplines documentrecentchange in the northern high-latitude environment.Prompted by predictions of an amplified response oftheArctic to enhanced greenhouse forcing, we present asynthesis of these observations. Pronounced winter andspring warming over northern continents since about 1970ispartly compensated by cooling over the northern NorthAtlantic. Warming is also evident over the centralArcticOcean. There is a downward tendency in sea ice extent,attended by warming and increased areal extent of theArctic Ocean's Atlantic layer. Negative snow coveranomalies have dominated over both continents sincethelate 1980s and terrestrial precipitation has increasedsince 1900. Small Arctic glaciers have exhibitedgenerally negative mass balances. While permafrost haswarmed in Alaska and Russia, it has cooled in easternCanada. There is evidence of increased plant growth,attended by greater shrub abundance and northwardmigration of the tree line. Evidence also suggeststhatthe tundra has changed from a net sink to a net sourceofatmospheric carbon dioxide.Taken together, these results paint a reasonablycoherent picture of change, but their interpretationassignals of enhanced greenhouse warming is open todebate.Many of the environmental records are either short,areof uncertain quality, or provide limited spatialcoverage. The recent high-latitude warming is also nolarger than the interdecadal temperature range duringthis century. Nevertheless, the general patterns ofchange broadly agree with model predictions. Roughlyhalfof the pronounced recent rise in Northern Hemispherewinter temperatures reflects shifts in atmosphericcirculation. However, such changes are notinconsistentwith anthropogenic forcing and include generallypositive phases of the North Atlantic and ArcticOscillations and extratropical responses to theEl-NioSouthern Oscillation. An anthropogenic effect is alsosuggested from interpretation of the paleoclimaterecord,which indicates that the 20th century Arctic is thewarmest of the past 400 years.
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Fluctuations of the land surface areas covered by Koeppen climates are analysed for the 1901 to 1995 period using trends and outliers as indicators of climate shift. Only the extreme climate zones of the global Tropics and of the Tundra (with the highly correlated northern hemisphere temperature) realise statistically significant shifts and outliers. There are nosignificant trends and outliers in the fluctuating ocean-atmosphere patterns (Pacific Decadal and North Atlantic Oscillations) and the highly correlated intermediate climate zones (dry, subtropical and boreal) of the surrounding continents.
The spatial and temporal variation of the C/D Köppen climate boundary in the central United States is examined for the period 1900 to 1999. Mean January temperature data from the U.S. Historical Climatology Network for 67 sites located between 37°N and 41.5°N latitude and 90°W and 100°W longitude are utilized. The variation of the boundary between the C and D climates (i.e., 26.6°F isoline) is illustrated for the entire 100-year study period and four quarter-century periods, as well as for individual decades (1900s, 1910s, …, 1980s, 1990s). For the quarter-century climatic periods, the latter two (1950 to 1974, 1975 to 1999) had C/D boundaries farther south (implying “colder” winters) compared to the positions for the first two quarter-century periods. The most anomalous feature for the decadal maps is the distinct southerly location of the C/D boundary for the recent decade of the 1970s. Although the C/D boundaries for the decades of the 1980s and 1990s generally are located slightly north of the 100-year overall mean location (implying “warmer” than average winter conditions), several earlier decades (e.g., 1900s, 1920s, 1930s) had even more northerly positions. Therefore, this study does not provide evidence of a trend toward wintertime warming and a northerly migration of the C/D climate boundary within the central United States. [Key words: climate change, climate classification.]
The Köppen climate classification system [Köppen, 1923] is a scheme that provides an objective numerical basis for defining regional climatic types based on temperature and precipitation. Through the years it has been used as a scientific and teaching tool for prescribing the general world pattern of climates. Here for the first time an evaluation of the system is performed by employing coextensive temperature and precipitation data over the N. Hemisphere for the last 140 years. First the global pattern of climate type sensitivity is obtained. From this pattern it is discovered that several climate types exhibit a rather strong variability. Since all climate types depend on temperature we then tested whether or not the above variability is due to the fact that over the last 140 years the global climate system exhibits a well documented positive temperature trend known as global warming. We found that the Köppen system is rather insensitive to the observed global warming and concluded that overall the system performs rather poorly over Europe and Asia whereas it appears adequate over N. America and N. Africa.
Long-term global gridded datasets of observed precipitation are essential for the analysis of the global water and energy cycle, its variability, and possible changes. Several institutions provide those datasets. In 2005 the Global Precipitation Climatology Centre (GPCC) published the so-called Variability Analysis of Surface Climate Observations (VASClimO) dataset. This dataset is especially designed for the investigation of temporal change and variability. To date, however, the GPCC has not published how this dataset has been produced. This paper aims to fill this gap. It provides detailed information on how stations are selected and how data are quality controlled and interpolated. The dataset is based only on station records covering at least 90% of the period 1951–2000. The time series of 9343 stations were used. However, these stations are distributed very inhomogeneously around the globe; 4094 of these stations are within Germany and France. The VASClimO dataset is interpolated from relative deviations of observed monthly precipitation, leading to considerably lower interpolation errors than direct interpolation or the interpolation of absolute deviations. The retransformation from interpolated relative deviations to precipitation is done with local long-term averages of precipitation interpolated from data of the Food and Agriculture Organization of the United Nations. The VASClimO dataset has been interpolated with a method that is based on local station correlations (LSC) that is introduced here. It is compared with ordinary kriging and three versions of Shepard's method. LSC outperforms these methods, especially with respect to the spatial maxima of interpolation errors.
Original field data show that forest species richness and tree density in the West African Sahel declined in the last half of the 20th century. Average forest species richness of areas of 4 km 2 in Northwest Senegal fell from 64 ± 2 species ca 1945 to 43 ± 2 species in 1993, a decrease significant at p < 0.001. Densities of trees of height ≥ 3 m declined from 10 ± 0.3 trees ha -1 in 1954 to 7.8 ± 0.3 trees ha -1 in 1989, also significant at p < 0.001. Standing wood biomass fell 2.1 t ha -1 in the period 1956-1993, releasing CO2 at a rate of 60 kgC person -1 yr -1 . These changes have shifted vege- tation zones toward areas of higher rainfall at an average rate of 500 to 600 m yr -1 . Arid Sahel species have expanded in the north, tracking a concomitant retraction of mesic Sudan and Guinean species to the south. Multivariate analyses identify latitude and longitude, proxies for rainfall and tempera- ture, as the most significant factors explaining tree and shrub distribution. The changes also de- creased human carrying capacity to below actual population densities. The rural population of 45 people km -2 exceeded the 1993 carrying capacity, for firewood from shrubs, of 13 people km -2 (range 1 to 21 people km -2 ). As an adaptation strategy, ecological and socioeconomic factors favor the natural regeneration of local species over the massive plantation of exotic species. Natural regenera- tion is a traditional practice in which farmers select small field trees that they wish to raise to matu- rity, protect them, and prune them to promote rapid growth of the apical meristem. The results of this research provide evidence for desertification in the West African Sahel. These documented impacts of desertification foreshadow possible future effects of climate change.
A database of monthly climate observations from meteorological stations is constructed. The database includes six climate elements and extends over the global land surface. The database is checked for inhomogeneities in the station records using an automated method that refines previous methods by using incomplete and partially overlapping records and by detecting inhomogeneities with opposite signs in different seasons. The method includes the development of reference series using neighbouring stations. Information from different sources about a single station may be combined, even without an overlapping period, using a reference series. Thus, a longer station record may be obtained and fragmentation of records reduced. The reference series also enables 1961–90 normals to be calculated for a larger proportion of stations. The station anomalies are interpolated onto a 0.5° grid covering the global land surface (excluding Antarctica) and combined with a published normal from 1961–90. Thus, climate grids are constructed for nine climate variables (temperature, diurnal temperature range, daily minimum and maximum temperatures, precipitation, wet-day frequency, frost-day frequency, vapour pressure, and cloud cover) for the period 1901–2002. This dataset is known as CRU TS 2.1 and is publicly available ( Copyright
A series of experiments was done using an atmospheric general circulation model to simulate climates from full glacial time at 18 ka (thousands of years before the present) to the present at 3000 year intervals, and at 126 ka, the previous interglacial period. A modified Kppen climate classification was developed to aid in the interpretation of the results of the circulation model experiments. The climate classification scheme permits the characterization of eleven distinct seasonal temperature and precipitation regimes. For the modern climate, the modified classification agrees well with a classification of natural vegetation zones, and provides an easily-assimilated depiction of climate changes resulting from the varying boundary conditions in the past. At 18 ka, the time of glacial maximum, 45% of the land surface had climate classifications different from the present. At 126 ka, a time when northern hemisphere summer radiation was much greater than at present owing to changes in the date of perihelion and tilt of the earth's axis, the corresponding difference was 32%. For all experiments -3 to 18 ka and 126 ka - only 30% of the land surface showed no change in climate classification from the present. Core areas showing no change included the Amazon basin, the northern Sahara and Australia.