https://orcid.org/0009-0004-1574-224X
Figures
Abstract
We introduce temperature zones based on average monthly temperatures that closely align with biome boundaries, intended for use in climate classification. This new system retains the simplicity and interpretability of existing classifications, such as those of Köppen-Geiger and Trewartha, while providing an improved fit to biome boundaries. Unlike previous classifications, we developed our system by analyzing contemporary climate and land cover datasets. Our classification focuses on temperature, but it is designed so that future analyses could combine our results with precipitation criteria from new or existing systems to offer a more comprehensive climate classification framework.
Citation: Xu K, Lauenroth W (2025) Biome-aligned temperature zones for interpretable climate classification via average monthly temperatures. PLOS Clim 4(6): e0000579. https://doi.org/10.1371/journal.pclm.0000579
Editor: Jingyu Wang, Nanyang Technological University, SINGAPORE
Received: January 21, 2025; Accepted: May 12, 2025; Published: June 12, 2025
Copyright: © 2025 Xu and Lauenroth. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data underlying the findings of this study are publicly available from the Earth Engine Data Catalog, accessible at https://developers.google.com/earth-engine/datasets, and the WorldClim official website, accessible at https://www.worldclim.org/data/worldclim21.html.
Funding: Funding for this initiative was provided by the Yale School of the Environment as general faculty support, without allocation of project-specific resources.
Competing interests: The authors have declared that no competing interests exist.
Introduction
An effective climate classification can serve many scientific and educational purposes. Rather than relying on arbitrary criteria, many existing systems were designed to align with biome and ecosystem boundaries [1–5]. Accurately distinguishing biome boundaries is crucial because biomes represent fundamental ecological units, reflecting the interplay of climate and life. Understanding these boundaries is essential for studying global biodiversity patterns, ecosystem functioning, and predicting the impacts of climate change on ecological systems [6]. A common priority of climate classifications is simplicity; classifications that are easily understood may be preferable to more complex ones with only a marginally better fit to biome boundaries.
Many existing climate classifications are based on two criteria, temperature and precipitation, which are used either alone or combined to classify a location. Arguably, the simplest such classification is a chart that plots biome types along axes of mean annual temperature and mean annual precipitation [1]. Although easy to understand, this classification lacks detail because it fails to consider seasonal variation in temperature and precipitation, which significantly affect vegetation. Similarly, the Thornthwaite and Holdridge life zone systems also aimed to use climate to describe dominant life forms but involve more complex calculations of potential evapotranspiration [2, 3], limiting their accessibility compared to simpler classifications.
Two of the most popular climate classifications are the Köppen-Geiger and Trewartha classifications. The Köppen-Geiger classification accounts for seasonality of temperature and precipitation [4]. However, it has its flaws; for instance, its definition of subtropical climates is overly broad, covering regions with both deciduous forests adapted to cold winters and evergreen forests adapted to year-round warmth. Another widely used climate classification, the Trewartha model, was designed to address some shortcomings of the Köppen-Geiger system, especially in providing more granularity in mid-latitudes [5]. Nevertheless, it too has notable inaccuracies, such as its placement of an oceanic climate between the subtropical and continental climates in the eastern U.S. and China, despite significant continentality.
Inspired by the contributions and limitations of prior climate classifications, we developed temperature zones that are highly correlated with biome locations, using a data-driven methodology guided by geospatial datasets of climate and land cover. Our temperature zones are based purely on average monthly temperatures—a decision that balances simplicity and detail. While more complex climate variables could be considered, monthly averages are readily available, easily interpretable, and capture the essential seasonal patterns relevant to broad-scale climate classifications [6]. Furthermore, the effectiveness of monthly average temperatures is supported by their established use in widely recognized climate classification systems like those of Köppen-Geiger and Trewartha, demonstrating their utility in capturing ecologically meaningful climate distinctions. Indeed, our analysis confirmed that these simple temperature metrics effectively establish critical biome thresholds. For example, criteria such as the mean temperature of the warmest or coldest months, and the number of months exceeding specific temperature values, successfully delineated major biome transitions from polar regions to the tropics—including those for ice cap, tundra, various forest types (boreal, hemiboreal, temperate, subtropical)—and also helped refine distinctions between continental and oceanic climates in the midlatitudes. The resulting temperature zones demonstrate a strong correlation with biome distributions and offer improved alignment with observed ecological boundaries over previous classifications.
Materials and methods
Given a region delineated by a temperature classifier (for example, all land regions where at most 2 months exceed a mean temperature of 8°C), this section outlines our approach to quantifying how well such a region corresponds to the actual location of a biome.
Temperature classifiers
Our study used the following classifiers based upon average monthly temperatures:
: mean temperature of the warmest month in degrees Celsius.
: mean temperature of the coldest month in degrees Celsius.
: annual temperature range in degrees Celsius, defined as
.
: number of months with a mean temperature of at least T degrees Celsius.
: number of months with a mean temperature below T degrees Celsius.
We selected these temperature classifiers due to their established use in widely-recognized systems such as Köppen-Geiger and Trewartha, as well as their simplicity, availability, and ease of visual derivation from a climate graph. A key modification in our approach was the inclusion of the annual temperature range ( −
), which proved critical for distinguishing continental and oceanic climates. Furthermore, we observed that a location’s annual average temperature—a metric prevalent in traditional systems—can be approximated as the maximum value of T satisfying
. This approximation is easier to derive from a climate graph.
Quantifying biome-classifier fit
We quantified how well a temperature classifier fits a biome using the following methodology:
Let the domain D be a portion of Earth’s surface that includes both the biome of interest, B, and the region that it is to be distinguished from, . Note that D should be defined to exclude surface regions that are irrelevant to the classification task. For example, if we wanted to separate land ice cap from all other land biomes, we would define the domain D to consist only of land and not water surfaces.
By applying an inequality threshold to a temperature classifier, for instance, , we divide D into a region C that satisfies the inequality, in this case the region that satisfies
, and a region
that does not satisfy the inequality, in this case the region that satisfies
. Our goal was to choose a threshold and classifier such that C corresponds roughly to B and
corresponds roughly to
. We formalize this approach as follows:
We define the false positive area (FPA) as the surface area of the region in the domain where and C overlap. We define the false negative area (FNA) as the surface area of the region in the domain where B and
overlap. If the inequality used for the threshold is < or
, as the threshold increases, FPA would increase and FNA would decrease. Meanwhile, if the inequality used for the threshold is > or
, as the threshold increases, FPA would decrease and FNA would increase. To ensure that both FPA and FNA are low, we aimed to minimize the maximum of false positive and negative area, or MFPNA (Fig 1). We also restricted our classifier to integer parameters and thresholds to ensure simplicity and ease of recall.
A temperature-based classifier defines region C. The goal is for C to accurately represent biome B by minimizing both the false positive area (FPA, where C incorrectly includes areas outside B) and the false negative area (FNA, where C fails to include parts of B). This is achieved by minimizing the maximum of FPA and FNA (MFPNA).
Datasets and data preprocessing
We constructed a global gridded dataset of average monthly air temperatures over land and ocean surfaces by combining WorldClim 2.1 monthly temperature averages [7] with ERA5 monthly 2-meter air temperature averages [8] for the years 1970-2000. The 2-meter air temperature variable is directly provided by the ERA5 reanalysis, and was used as such in our study without derivation from pressure-level data. The resulting dataset incorporates high-resolution WorldClim data over all continental regions and uses bicubic-interpolated, smoothed ERA5 data only for oceanic regions.
We sourced ground truth data for biome locations from the MODIS MCD12Q1 v061 land cover map of 2001 [9] and the Copernicus CGLS-LC100 land cover map of 2015 [10]. In regions dominated by buildings and cultivated land, we used a potential natural vegetation (PNV) map based on the BIOMES 6000 dataset [11]. We also employed the GTOPO30 digital elevation model [12] to exclude highland areas in certain analyses.
We performed all calculations in Google Earth Engine [13].
Results
In this section, we present several biome definitions and the temperature classifiers that best correspond to them. Specifically, we found temperature thresholds associated with key biome boundaries, including ice cap and tundra, tundra and boreal forest, boreal and hemiboreal forest, hemiboreal and temperate forest, temperate and subtropical forest, subtropical and tropical latitudinal zones, and midlatitude land and water regions.
Ice cap/tundra classifier
We defined ice cap as all land regions with permanent ice and snow. A temperature classifier that separates ice cap from tundra is equivalent to one that separates ice cap from all other biomes, as ice cap is adjacent to tundra. Thus, we defined the domain D as all land on Earth, and the biome B as all land with permanent ice and snow (MODIS class 15).
Since ice cap regions are marked by year-round cold conditions without sufficient warmth to melt the ice, we tested hypotheses of the forms and
for integer values of T and N. The classifier with the least MFPNA was
with 246,000 and 269,000 square kilometers of FPA and FNA (Fig 2). All other classifiers of these forms yielded over 300,000 square kilometers of MFPNA. Altogether, these results suggest that
is the optimal simple classifier for distinguishing ice cap from non-ice-cap land regions. This threshold is slightly higher than that of the ice cap climate of Köppen-Geiger and Trewartha, defined as
, which yields a FPA and FNA of 35,000 and 611,000 square kilometers of FPA and FNA. Our findings suggested that land ice can occur in regions where the mean temperature of the warmest month is slightly above freezing, rather than strictly below freezing.
Colors represent true positive (green), true negative (blue), false positive (red), false negative (orange), and out-of-domain (white) regions. Basemap from Natural Earth (public domain). Data: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/. Terms: https://www.naturalearthdata.com/about/terms-of-use/.
Tundra/boreal forest classifier
We defined polar land regions as ice cap and tundra, and all other land cover types as non-polar (including boreal forest). Thus, a temperature classifier that separates polar and non-polar biomes also effectively separates tundra from boreal forest. We defined the domain D as all land with an elevation below 1000 m that is not cultivated land (Copernicus class 40), urban build-up (Copernicus class 50), or herbaceous wetland (Copernicus class 90), hence eliminating ambiguous land regions that are irrelevant to classification. The elevation filter ensured our classifier focused on separating polar tundra and boreal forest, rather than separating alpine tundra from subalpine regions. We defined the biome B as the subset of D that is tundra or ice cap—specifically, the subset of D where and land cover is either permanent snow and ice (Copernicus class 70), herbaceous vegetation (Copernicus class 30), bare/sparse vegetation (Copernicus class 60), or moss and lichen (Copernicus class 100). The temperature threshold
was crucial because the Copernicus dataset does not distinguish tundra from equatorward grasslands. This specific threshold was chosen based on the observation that the stricter threshold of
, used in the Köppen-Geiger and Trewartha classifications, often misclassifies large tundra areas as subpolar (as shown later). Meanwhile, the less restrictive threshold of
, used in the Köppen-Geiger and Trewartha classifications, would include some temperate grassland on the equatorward fringes of the boreal forest. Our chosen threshold of
includes nearly all true tundra in B while still curtailing the inclusion of temperate grassland.
Since a distinguishing feature of the tundra is a short growing season, we tested hypotheses of the form for integer values of T and N. Classifiers that yielded a particularly low MFPNA included
with 1,015,000 and 652,000 square kilometers of FPA and FNA (Fig 3),
with 482,000 and 1,501,000 square kilometers of FPA and FNA,
with 665,000 and 1,623,000 square kilometers of FPA and FNA, and
with 551,000 and 1,649,000 square kilometers of FPA and FNA. In comparison, Köppen-Geiger and Trewartha both defined a polar climate (tundra or ice cap) as one satisfying
, which yields 37,000 and 3,984,000 square kilometers of FPA and FNA, indicating a bias towards false negatives. We instead recommend the classifier
or
to distinguish tundra from boreal forest.
Colors represent true positive (green), true negative (blue), false positive (red), false negative (orange), and out-of-domain (white) regions. The domain did not include Antarctica and places north of latitude due to missing data, but these regions are expected to lie in the true positive regions and thus not significantly affect misclassified land areas. Basemap from Natural Earth (public domain). Data: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/. Terms: https://www.naturalearthdata.com/about/terms-of-use/.
Boreal/hemiboreal forest classifier
We defined boreal forest as the biome consisting of predominantly conifers found directly equatorward of the tundra. To find a temperature classifier that distinguished between boreal forest and the more equatorward biomes, we defined the domain D as all land with an elevation below 1000 m that is not cultivated land (Copernicus class 40), urban build-up (Copernicus class 50), or herbaceous wetland (Copernicus class 90), thus eliminating ambiguous land regions that are irrelevant to classification. The elevation filter excluded alpine regions in our analysis. We defined the biome B as the subset of D composed of boreal forest or a more polar biome—specifically, the subset of D where and land cover consists of shrubs (Copernicus class 20), needleleaf forest (Copernicus classes 111, 112, 121, and 122), or a more poleward vegetation scheme—permanent snow and ice (Copernicus class 70), herbaceous vegetation (Copernicus class 30), bare/sparse vegetation (Copernicus class 60), or moss and lichen (Copernicus class 100). We applied the temperature filter
since the Copernicus data does not differentiate boreal forest from temperate or more equatorward coniferous forests. This filter relaxes the Köppen-Geiger and Trewartha criterion of a subpolar climate (
) to include more of the equatorward fringes of the boreal forest. We did not include hemiboreal forest—a region of mixed coniferous and deciduous trees located equatorward of the boreal forest and poleward of the temperate deciduous forest—in our definition of B.
Since boreal forest is marked by a limited growing season, we tested hypotheses of the form for integer values of T and N. Classifiers yielding a particularly low MFPNA included
with 1,340,000 and 1,334,000 square kilometers of FPA and FNA (Fig 4),
with 1,412,000 and 1,675,000 square kilometers of FPA and FNA, and
with 2,276,000 and 714,000 square kilometers of FPA and FNA. The classifier
also coincides with that used by Köppen-Geiger and Trewartha to distinguish subpolar from temperate climates, but we found two other classifiers with similar performance.
Colors represent true positive (green), true negative (blue), false positive (red), false negative (orange), and out-of-domain (white) regions. Basemap from Natural Earth (public domain). Data: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/. Terms: https://www.naturalearthdata.com/about/terms-of-use/.
Hemiboreal/temperate forest classifier
We defined the hemiboreal forest as the region with a mixture of conifers and deciduous trees located between the largely coniferous boreal forest and the largely deciduous temperate deciduous forest. Since the distribution of vegetation is often obfuscated by human activity in these regions, we instead used potential natural vegetation data to define our domain and biome. We defined the domain D as all land regions with an elevation below 1000 m where PNV is either cool mixed forest (PNV class 9) or temperate deciduous broadleaf forest (PNV class 13), encompassing all areas with either hemiboreal or temperate deciduous forest. Furthermore, we defined the biome B as the subset of D that is cool mixed forest (PNV class 9).
We tested hypotheses of the form for integer values of T and N. Classifiers yielding a particularly low MFPNA included
with 4,060,000 and 4,401,000 square kilometers of FPA and FNA (Fig 5),
with 3,001,000 and 4,735,000 square kilometers of FPA and FNA, and
with 4,313,000 and 4,895,000 square kilometers of FPA and FNA.
Colors represent true positive (green), true negative (blue), false positive (red), false negative (orange), and out-of-domain (white) regions. Basemap from Natural Earth (public domain). Data: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/. Terms: https://www.naturalearthdata.com/about/terms-of-use/.
Temperate/subtropical forest classifier
We defined the subtropical forest as the region of predominantly evergreen forest found equatorward of the temperate deciduous forest. Examples of such forests include the southeastern conifer forests of the U.S. and the evergreen broadleaf forests of southern China. Since natural vegetation in these regions is obfuscated by human activity, we used potential natural vegetation data. To find a temperature classifier distinguishing temperate deciduous forest from subtropical forest, we defined the domain D as all land regions with an elevation below 1000 m where PNV is either temperate deciduous broadleaf forest (PNV class 13) or warm-temperate evergreen broadleaf and mixed forest (PNV class 4). Furthermore, we defined the biome B as the subset of D that is warm-temperate evergreen broadleaf and mixed forest (PNV class 4).
Hypothesizing that subtropical forest can be distinguished by a limited cold season, we tested classifiers of the form and
for integer values of T and N. For classifiers of the form
, those yielding the lowest MFPNA included
with 794,000 and 1,018,000 square kilometers of FPA and FNA, as well as
with 1,285,000 and 517,000 square kilometers of FPA and FNA. Classifiers of the form
yielded an even lower MFPNA; those yielding the lowest MFPNA were
with 536,000 and 620,000 square kilometers of FPA and FNA (Fig 6),
with 303,000 and 631,000 square kilometers of FPA and FNA,
with 726,000 and 274,000 square kilometers of FPA and FNA, and
with 742,000 and 561,000 square kilometers of FPA and FNA.
Colors represent true positive (green), true negative (blue), false positive (red), false negative (orange), and out-of-domain (white) regions. Basemap from Natural Earth (public domain). Data: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/. Terms: https://www.naturalearthdata.com/about/terms-of-use/.
Subtropical/tropical classifier
Due to the blurry distinction between tropical and subtropical vegetation types, we used a latitude-based definition of the tropics as regions between the Tropic of Cancer and Tropic of Capricorn. This definition encompasses both land and water, enabling us to test classifiers on land-only, water-only, or land-and-water regions in the tropics.
Land and water.
To find a classifier that differentiated between tropical and non-tropical regions on both land and water, we defined the domain D as all locations on Earth’s surface with an elevation of at most 1000 m. Furthermore, we defined the biome B as the subset of D located between the Tropic of Cancer and Tropic of Capricorn. Since a tropical climate is marked by year-round warmth, we tested hypotheses of the form for integer values of T. Classifiers that yielded the lowest MFPNA were
with 15,316,000 and 14,770,000 square kilometers of FPA and FNA (Fig 7),
with 8,382,000 and 21,858,000 square kilometers of FPA and FNA, and
with 23,714,000 and 9,673,000 square kilometers of FPA and FNA.
Colors represent true positive (green), true negative (blue), false positive (red), false negative (orange), and out-of-domain (white) regions. Basemap from Natural Earth (public domain). Data: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/. Terms: https://www.naturalearthdata.com/about/terms-of-use/.
Land.
To find a classifier that differentiates between tropical and non-tropical land regions, we defined the domain D as all land regions with an elevation of at most 1000 m, and the biome B as the subset of D located between the Tropic of Cancer and Tropic of Capricorn. Testing hypotheses of the form for integer values of T, those yielding the lowest MFPNA were
with 5,373,000 and 3,931,000 square kilometers of FPA and FNA,
with 3,731,000 and 5,836,000 square kilometers of FPA and FNA, and
with 7,693,000 and 2,268,000 square kilometers of FPA and FNA.
Water.
To find a classifier that differentiates between tropical and non-tropical water regions, we defined the domain D as all water regions with an elevation of at most 1000 m, and the biome B as the subset of D located between the Tropic of Cancer and Tropic of Capricorn. Testing hypotheses of the form for integer values of T, those that yielded the lowest MFPNA were
with 8,372,000 and 11,207,000 square kilometers of FPA and FNA,
with 15,089,000 and 6,918,000 square kilometers of FPA and FNA, and
with 3,435,000 and 18,490,000 square kilometers of FPA and FNA.
Midlatitude land/water classifier
The Köppen-Geiger and Trewartha classifications identify oceanic and continental subtypes for midlatitude climates, specifically temperate and subpolar. This choice is reasonable, as given the minimal biome diversity in polar regions and reduced continentality in subtropical and tropical regions, a land-water classifier is arguably more relevant in the midlatitudes. To find an improved land/water classifier for the midlatitudes, we defined the domain D as all parts of Earth’s surface between and
latitude or between
and
degrees latitude. We did not add an elevation filter, as elevation does not significantly affect continentality. We further defined the biome B as all land surfaces in D (MODIS class not equal to 17).
We hypothesized that continental regions can be simply distinguished by greater variation between summer and winter temperatures, testing classifiers of the form for integer values of T. Classifiers of this form with the lowest MFPNA included
with 7,822,000 and 7,913,000 square kilometers of FPA and FNA (Fig 8),
with 9,216,000 and 6,482,000 square kilometers of FPA and FNA, as well as
with 6,638,000 and 9,260,000 square kilometers of FPA and FNA. Meanwhile, under the same domain and biome definitions, the Köppen-Geiger criterion for a continental climate,
, yields much greater FPA and FNA areas of 18,757,000 and 16,621,000 square kilometers. This indicates that annual temperature range is a more reliable indicator of continentality than the mean temperature of the coldest month.
Colors represent true positive (green), true negative (blue), false positive (red), false negative (orange), and out-of-domain (white) regions. Basemap from Natural Earth (public domain). Data: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/. Terms: https://www.naturalearthdata.com/about/terms-of-use/.
Discussion and conclusion
Our temperature zones were highly correlated with biome locations and represented a clear improvement on past analyses (Fig 9). One of the challenges that this and any analysis of the relationship between climate and biome locations must address is that changes in land cover lag behind changes in climate, but the exact duration of this time lag is uncertain and very likely varies across different biomes [14]. Hence, it is challenging to determine which historical period’s average temperatures, if held in a long-term steady state, would result in the land cover reflected by the datasets in this paper. Our approach used data from 1970-2000.
Both Köppen-Geiger and Trewartha defined an ice cap climate as one satisfying . We propose adhering to this criterion, despite
yielding a slightly lower MFPNA, due to its simplicity and potential to account for time-lag.
Both Köppen-Geiger and Trewartha defined a polar climate, corresponding to tundra and ice cap, as one satisfying . We found that this definition is prone to false negatives, and instead recommend the classifier
. Even though
yielded a slightly lower MFPNA, we preferred a slightly more conservative classifier to account for potential time-lag.
Both Köppen-Geiger and Trewartha used the criterion of to separate polar and subpolar biomes from temperate biomes. We found that this threshold yields an acceptable MFPNA, and maintain its usage.
However, in temperate zones, the warm-summer continental, hot-summer continental, oceanic, and subtropical temperature classifications of Köppen-Geiger align poorly with temperate biomes. Instead, we propose a hemiboreal or cool temperate zone defined by , corresponding to regions with a mixture of deciduous trees resembling more equatorward forests and conifers resembling the more poleward boreal forest. We also propose a regular temperate zone for regions satisfying
but not satisfying subtropical or tropical criteria, corresponding to more equatorward temperate regions with mostly deciduous forests. In both temperate and subpolar regions, we find that the simple classifier
is suitable for distinguishing continental from oceanic regions, vastly outperforming the Köppen-Geiger definition.
We differ from both Köppen-Geiger and Trewartha in our definition of a subtropical climate. We believe that an intuitive definition of a subtropical climate is one that is, in colloquial terms, “never too cold” and “warm on average.” As such, we define a subtropical climate as one satisfying both and
, but not satisfying the criterion for a tropical climate. We define two subtropical subtypes: subtropical warm, where
, and subtropical hot, where
. These categories are designed so that a temperate continental climate transitions into a subtropical hot climate if the mean temperatures of all months increased by the same amount, while a temperate oceanic climate transitions into a subtropical warm climate under the same transformation.
Finally, we find that the tropical threshold of used by both the Köppen-Geiger and Trewartha classifications performs fairly well for distinguishing between non-highland terrestrial regions in the tropics from those in the subtropics, and maintain its usage.
Plotting our temperature zones on a map reveals several major global climatic phenomena. In the Atlantic Ocean, the Gulf Stream shifts the subtropical warm climate type into far northerly latitudes. Meanwhile, cold ocean currents off the west coasts of North and South America sometimes shift subtropical climates in coastal regions into strictly tropical latitudes. In the Northern Hemisphere, the continental climates extend farther south on the eastern sides of their respective continents, likely due to decreasing oceanic moderation of temperature. Continental climates are virtually nonexistent in the Southern Hemisphere, likely due to a lack of wide land regions enabling a large annual temperature range. For the same reason, bands of oceanic climates in the Northern Hemisphere feature more latitudinal variation than in the Southern Hemisphere.
Future research will likely focus on combining these temperature zones with precipitation zones, allowing further classification into well-known humidity-based subtypes such as arid, semi-arid, humid, Mediterranean, and monsoonal. Another direction of future research is mapping historic and forecasted changes of these temperature zones to visualize the effects of climate change.
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