The most important indicator of global warming, by far, is the land and sea surface temperature record. This has been criticized in several ways, including the choice of stations and the methods for correcting systematic errors. The Berkeley Earth Surface Temperature study sets out to to do a new analysis of the surface temperature record in a rigorous manner that addresses this criticism. We are using over 39,000 unique stations, which is more than five times the 7,280 stations found in the Global Historical Climatology Network Monthly data set (GHCN-M) that has served as the focus of many climate studies.
Our aim is to resolve current criticism of the former temperature analyses, and to prepare an open record that will allow rapid response to further criticism or suggestions. Our results include not only our best estimate for the global temperature change, but estimates of the uncertainties in the record.
Our aim is to resolve current criticism of the former temperature analyses, and to prepare an open record that will allow rapid response to further criticism or suggestions. Our results include not only our best estimate for the global temperature change, but estimates of the uncertainties in the record.
he Berkeley Earth Surface Temperature Study has created a preliminary merged data set by combining 1.6 billion temperature reports from 15 preexisting data archives. Whenever possible, we have used raw data rather than previously homogenized or edited data. After eliminating duplicate records, the current archive contains 39,390 unique stations. This is more than five times the 7,280 stations found in the Global Historical Climatology Network Monthly data set (GHCN-M) that has served as the focus of many climate studies. The GHCN-M is limited by strict requirements for record length, completeness, and the need for nearly complete reference intervals used to define baselines. We have developed new algorithms that reduce the need to impose these requirements (see methodology), and as such we have intentionally created a more expansive data set.
We performed a series of tests to identify dubious data and merge identical data coming from multiple archives. In general, our process was to flag dubious data rather than simply eliminating it. Flagged values were generally excluded from further analysis, but their content is preserved for future consideration.
Specifically, the Berkeley Earth study concludes that:
•The urban heat island effect is locally large and real, but does not contribute significantly to the average land temperature rise. That's because the urban regions of Earth amount to less than 1% of the land area.
•About 1/3 of temperature sites around the world reported global cooling over the past 70 years (including much of the United States and northern Europe). But 2/3 of the sites show warming. Individual temperature histories reported from a single location are frequently noisy and/or unreliable, and it is always necessary to compare and combine many records to understand the true pattern of global warming.
•The large number of sites reporting cooling might help explain some of the skepticism of global warming," Rohde commented. "Global warming is too slow for humans to feel directly, and if your local weather man tells you that temperatures are the same or cooler than they were a hundred years ago it is easy to believe him." In fact, it is very hard to measure weather consistently over decades and centuries, and the presence of sites reporting cooling is a symptom of the noise and local variations that can creep in. A good determination of the rise in global land temperatures can't be done with just a few stations: it takes hundreds -- or better, thousands -- of stations to detect and measure the average warming. Only when many nearby thermometers reproduce the same patterns can we know that the measurements were reliably made.
•Stations ranked as "poor" in a survey by Anthony Watts and his team of the most important temperature recording stations in the U.S., (known as the USHCN -- the US Historical Climatology Network), showed the same pattern of global warming as stations ranked "OK." Absolute temperatures of poor stations may be higher and less accurate, but the overall global warming trend is the same, and the Berkeley Earth analysis concludes that there is not any undue bias from including poor stations in the survey.
Specifically, the Berkeley Earth study concludes that:
•The urban heat island effect is locally large and real, but does not contribute significantly to the average land temperature rise. That's because the urban regions of Earth amount to less than 1% of the land area.
•About 1/3 of temperature sites around the world reported global cooling over the past 70 years (including much of the United States and northern Europe). But 2/3 of the sites show warming. Individual temperature histories reported from a single location are frequently noisy and/or unreliable, and it is always necessary to compare and combine many records to understand the true pattern of global warming.
•The large number of sites reporting cooling might help explain some of the skepticism of global warming," Rohde commented. "Global warming is too slow for humans to feel directly, and if your local weather man tells you that temperatures are the same or cooler than they were a hundred years ago it is easy to believe him." In fact, it is very hard to measure weather consistently over decades and centuries, and the presence of sites reporting cooling is a symptom of the noise and local variations that can creep in. A good determination of the rise in global land temperatures can't be done with just a few stations: it takes hundreds -- or better, thousands -- of stations to detect and measure the average warming. Only when many nearby thermometers reproduce the same patterns can we know that the measurements were reliably made.
•Stations ranked as "poor" in a survey by Anthony Watts and his team of the most important temperature recording stations in the U.S., (known as the USHCN -- the US Historical Climatology Network), showed the same pattern of global warming as stations ranked "OK." Absolute temperatures of poor stations may be higher and less accurate, but the overall global warming trend is the same, and the Berkeley Earth analysis concludes that there is not any undue bias from including poor stations in the survey.
We filtered and merged the data archives using the following steps:
1- Duplicate filter: We first separately searched each archive for multiple copies of the same record and eliminated the duplicates.
2- Data split: Each unique record was broken up into fragments having no gaps longer than 1 year. Each fragment was then treated as a separate record for filtering and merging. Note however that the number of stations is based on the number of unique locations, and not the number of record fragments.
3- Bad values filter: We flagged and excluded from further study values that had pre-existing indicators of data quality problems associated with instrumental error, in-filling of missing data, and/or post-hoc manipulations. We further removed values that exceeded global climate extremes (e.g. +5000 F).
4- Repetition filter: We tested for runs of repeated values, a common sign of in-filling missing days, and flagged repeated values exceeding an empirical 99.9% threshold for non-randomness.
5- Local outlier filter: We tested for and flagged values that exceeded a locally determined empirical 99.9% threshold for normal climate variation in each record.
6- Temperature consistency filter: We required that the minimum temperature (Tmin) be strictly less than the maximum temperature (Tmax) for each measurement. We further required that any reported average or instantaneous temperature (Tavg and Tobs) be between the reported max and min, inclusive.
7- Initial merge: Using nearby locations and matching station ID codes, we tested for the presence of identical data in multiple archives. Records that had identical content for at least 90% of values were then merged. Small segments of non-identical content within otherwise equivalent records were flagged and also carried forward.
8- Regional filter: For each record, the 21 nearest neighbors having at least 5 years of record were located. These were used to estimate a normal pattern of seasonal climate variation. After adjusting for changes in latitude and altitude, each record was compared to its local normal pattern and 99.9% outliers were flagged. Simultaneously, a test was conducted to detect long runs of data that had apparently been miscoded as Fahrenheit when reporting Celsius. Such values, which might include entire records, would be expected to match regional norms after the appropriate unit conversion but not before.
9-Second merge: Monthly time series were constructed from daily values with both a version using all values and a version using only non-flagged values. These monthly synthesis records were then compared to the values in data archives that reported only monthly data. Duplicates were found as before and merged.
9-Second merge: Monthly time series were constructed from daily values with both a version using all values and a version using only non-flagged values. These monthly synthesis records were then compared to the values in data archives that reported only monthly data. Duplicates were found as before and merged.
10- Site reduction: Though a majority of all station repetitions are identified by the presence of duplicated data, in a significant number of cases the presence of pre-existing data manipulations inhibited our tests for data duplication. We designed several tests based on location, name, and id codes to identify matching sites with somewhat dissimilar data. These were then consolidated as single stations having multiple data series.
11- Best value series: “Best value” time series were formed by averaging across multiple records when they existed at the same site. In addition, flagged values were dropped and previously manipulated GHCN-M and Hadley Centre data was ignored in favor of other data sources when possible. These series are expected to the primary records for most future studies, but the fully-flagged and multi-valued records will also be preserved and made available for more detailed analyses.
12- Seasonality removed series: Finally, non-seasonal series were created by determining the mean seasonal cycle at each location and subtracting this from the best value data.
We are currently preparing a detailed write up of the process used for the data filtering and merge.
We are currently preparing a detailed write up of the process used for the data filtering and merge.
 |
| The following chart shows the annual land-surface average temperature using a 12 month moving average of surface temperatures over land. Anomalies are relative to the Jan 1950 - December 1979 mean. The grey band indicates 95% statistical and spatial uncertainty interval. |
 |
| The following chart shows the decadal land-surface average temperature using a 10-year moving average of surface temperatures over land. Anomalies are relative to the Jan 1950 - December 1979 mean. The grey band indicates 95% statistical and spatial uncertainty interval. |
| Berkeley Earth Data |
| The Berkeley Earth merged data set can be accessed from the links below. |
| Berkeley Earth Analysis Code |
| The Berkeley Earth analysis code is available from the link below. |
|
No hay comentarios:
Publicar un comentario