Climate change is often communicated by looking at the global average temperature. But a global average might not mean much to the average person. How the climate is likely to change specifically where people live is, in most cases, a much more important consideration.

Carbon Brief has combined observed temperature changes with future climate model projections to show how the climate has changed up to present day, but also how it might change in the future for every different part of the world.

To do this, the world has been broken up into “grid cells” representing every degree latitude and every degree longitude. This results in 64,800 grid cells, which are typically about 100 kilometers wide. (In reality, they are a bit larger at the equator and smaller close to the poles.)

The map overlay on the interactive above shows the amount of warming to expect in each grid cell based on futureRepresentative Concentration Pathway(RCP) scenarios developed by climate scientists. These four scenarios represent different possible future emission trajectories. They range from the low-warmingRCP2.6scenario, which keeps global warming from the pre-industrial era to below 2C, up to a high-warmingRCP8.5scenario that would likely see global temperatures rise to above 4C.

How to use this map

Clicking on any grid cell brings up a sidebar showing the historical temperature record for that location between 1850 and 2017, both by year (in white) and with a smoothed average using 10 years of data (in red). An additional plot shows the future warming projected for that location under the four different RCP scenarios from 2000 through to 2100 – in purple, red, orange and yellow. Both historical and future temperatures are shown relative to a 1951-1980 baseline period.

The sidebar indicates both how much warming has been experienced between the first 30 years of the record and the past decade. Additionally, it shows how much warming is expected by 2100, relative to the baseline period.

Specific locations can also be typed into the search bar in the upper left corner. The past observed and future projected temperatures for each location can be downloaded by clicking on the “download csv” link. Clicking on the “home” symbol on the left will reset the interactive back to its default starting point. (Note: Users with laptops or other small screens may want to zoom out on their browsers for a better view of the map.)

Methodology and data sources

Observed temperatures

Temperatures based on land and ocean observations were obtained from theBerkeley Earth Surface Temperature Project’sone-degree latitude by one-degree longitude gridded monthly averagetemperature fields(note: large file download). These were converted into annual averagetemperature anomalies相对于1951 - 1980年巴塞尔协议ine period.

These temperature estimates use observations from around 30,000 land monitoring stations, as well as thousands of ships, buoys and other monitoring systems over the ocean. Berkeley Earth uses the UK Met Office’sHadSST3ocean temperature record as the basis for its ocean temperatures.

Observational data is available back to 1850, though for any given location data may not go back that far. Data is available from at least 1900 for most locations except Antarctica, where data is only available starting in 1950 when measurements on that continent began.

Berkeley Earth land data ishomogenised– adjusted to correct for station moves,instrument changes,time of observation changesandother disruptionsthat stations have experienced over the past 150 years. Ocean temperature records are similarly adjusted to account for changes in the way ocean temperatures are measured, frombuckets thrown over the sideof ships through toengine-room intake valvesandautomated buoysin modern times.

These adjustments have a relatively small impact on temperatures after 1950, as discussed in theCarbon Brief explaineron temperature adjustments. The overall effect of adjustments is toincrease temperatures globallyprior to 1950, reducing the amount of long-term warming in the record compared to the raw readings.

Future models

Future temperature projections are taken from theCoupled Model Intercomparison Project 5(CMIP5) multi-model average surface air temperature for each RCP scenario. CMIP5 features around 38 different climate models, though some of these represent variations of the same underlying model with different aspects included. One run from each model was used in calculating the multi-model average, with the model temperature fields obtained fromKNMI Climate Explorer．

These multi-model average values aredownscaled– increased in spatial resolution – to a one-degree latitude by one-degree longitude resolution to be comparable to the observations. They are converted into anomalies with respect to a 1951-1980 baseline, then aligned to the observations over the 20-year period from 1999-2018 to show the changes expected from present. Model data is shown between 2000 and 2100 in the sidebar for each grid cell.

Understanding uncertainty

Both observational temperature estimates and future projected temperature changes are subject to uncertainty. Observational uncertainties in historical temperature records from Berkeley Earth are shown in the sidebar.

Observational uncertainties can arise from a number of different factors. Incomplete coverage of observations across the Earth’s surface means that sometimes temperature anomalies in a location have to be estimates from nearby land stations or ocean measurements. The Berkeley Earth dataset uses a technique called “kriging” to create globally complete estimates of both temperature anduncertaintyfrom observations at specific locations.

Future climate model projections also include significant uncertainties, chief among them thesensitivity of the climateto increased CO2. TheCMIP5 modelsfeatured in themost recent IPCC reportestimates climate sensitivity at between 2.1C and 4.7C per doubling of atmospheric CO2 levels, with an average sensitivity of 3.1C. The multi-model average projections shown in the sidebar only reflect this 3.1C value; users interested in the results of individual models with higher or lower sensitivity will have to use a tool such asKNMI Climate Explorerto view those results.

Individual models also show a lot more year-to-year variability than the multi-model average shown in the sidebar. Individual models have short-term variability driven by factors includingEl Niño and La Niñaevents that result in some years warmer or cooler than others. However, this short-term variability occurs at different times in different models and is largely averaged out in the multi-model average.

Technical details

代码用来计算过去的观察和将来时e projected temperatures for each of the 64,800 grid cells is available onGitHuband free for reuse or modification.

Temperature observations from the Berkeley Earth gridded one-degree latitude by one-degree longitudenetCDF fileare imported and converted into annual anomalies with respect to a 1951-1980 baseline period. A smoothed average is produced using a local regression (LOWESS) approach that uses a 10-year period for calculation.

Future temperature projections from the CMIP5 multi-model mean are obtained fromKNMI Climate Explorer．这些是统计na的缩减规模tive 2.5-degree latitude by 2.5-degree longitude resolution to a one-degree latitude by one-degree longitude usingbilinear interpolation– an average of nearby values. Model data is then converted into temperature anomalies with respect to a 1951-1980 baseline period. Finally, models are aligned with observations over the prior 20-year period (1999-2018) to better represent the expected change from present values.

A location name is assigned to each grid cell through a multi-step process. First, grid cell locations are geolocated using thereverse_geocoderpython library. This provides information on the city, state and country closest to the grid cell’s centre. An additional “countries.geojson” file is used to identify areas over the ocean or in unpopulated areas, such as Antarctica and the high Arctic.

Finally, the centres of the grid cells are referenced against alist of all citieswith a population exceeding 20,000. The name of the largest city in the grid cell is selected when multiple are present. This avoids assigning grid cell names to the settlement that happens to be closest to the geographic center of the grid cell irrespective of population.