Can we use satellites
instead of ground stations to measure temperature and estimate climate response functions?
Guest post by Sam Heft-Neal
Temperature
data are commonly used to estimate the sensitivity of societally relevant
outcomes to ongoing climate changes. In order to derive a temperature measure,
researchers typically interpolate nearby ground station data or use one of the
publicly available global gridded data products put out by groups like CRU or UDel.
However, Max and Sol and Wolfram and Adam have shown that there are a number of pitfalls associated with
using interpolated station data to measure temperature in areas with
sparse coverage. Temperature estimates far from weather stations, or in areas
where stations go on and offline frequently, are subject to substantial
measurement error that can bias impact estimates. This problem is particularly
acute in tropical regions, where many of the largest climate impacts are
expected to occur, but where we still have a limited understanding of
key climate/society relationships.
David, Marshall and I have a new paper
that evaluates whether using temperature measures derived only from satellites can be used to estimate climate
response functions. Several
satellites measure surface emission of thermal energy, which can be converted
into estimates of skin surface temperature (Ts) – a product that MODIS has
provided at 1km resolution daily for nearly a decade. Past studies have
evaluated agreement between MODIS and air temperature measured by weather
stations (Ta) on daily time scales, often finding weak correlations for daytime
temperatures because factors other than Ta, such as cloudiness and soil
moisture, can affect Ts. However, these results could be of limited relevance
for our purpose, since the climate response functions we care about usually rely on year-to-year
variations in seasonally aggregated measures of temperature exposure, and
correlations between station and satellite data tend to increase as the period
of aggregation lengthens.
In
order to test whether Ts could be used in place of Ta to study temperature
impacts, we revisited three previous studies (each led by a different G-Feed blog
contributor) that had used standard measures of Ta to study climate impacts. For each
study we replicated the analysis with both Ta and Ts and compared model
performance. The first study
examined temperature effects on maize yields in Africa using historical field
trial data from plots under optimal management and plots under drought
management. The second study looked
at county level maize yields in the U.S. and the third study,
which we selected to look at an application outside of agriculture, estimated
temperature effects on county-level GDP in the U.S..
In
order to assess model performance we calculated out-of-sample prediction error
by repeatedly estimating the models on randomly selected 75% subsets of
locations, predicting values for the 25% of locations that had been excluded from
estimation, and calculating the RMSE of out-of-sample predicted values relative
to actual values. In each case we found similar relationships between
temperature and the outcome of interest (panels 1 and 2 below). For both types
of African maize field trials we actually found that models with satellite
temperature had lower prediction
error than models with ground station temperature. However, when we compared
models in the U.S. (panel 3) with its high density of stations, the models had
very similar predictive powers. This finding suggests that Ts is more useful in
regions with poor station coverage where Ta is measured with significant levels
of error.
 |
| Response functions for maize yields in Africa (left 2 plots) and maize yields in US (right plots). Orange is surface temperature from MODIS, blue is air temperature from stations. |
While
Ts predicts crop yields well, it is less clear whether it could be used to
estimate response functions for non-agricultural applications like GDP. The GDP
study also differs from the agricultural studies because instead of using
seasonal averages for temperature we used temperature bins. The satellite data
we used were 8-day composites so for every observation that fell into a given temperature
bin we assigned eight days to that bin (in other words we assumed constant – or
at least within a constant bin - temperature for each 8-day period). Even with
this assumption, the model with Ts (somewhat surprisingly to us) reproduced a
similar non-linear response function over most of the temperature support,
particularly at the upper end of the temperature distribution where income
appears to be most sensitive to temperature.
 |
| Replication of Deryugina and Hsiang (2014), using ground stations (left) and MODIS (right). |
Lastly,
as a final comparison, we compared the aggregated impacts from 1◦C warming estimated with both models.
In doing so we again find similar estimates for all applications.
 |
| Impact of +1C warming for different models. |
This
overall consistency is perhaps somewhat surprising, given the often low
correlations between anomalies in Ts and Ta at the daily or 8-day time scale. In
the paper we argue that there are at least four reasons Ts could outperform Ta:
1. Some of the “noise” in Ts vs. Ta relationships
stems from errors in the Ta measures, particularly in regions such as Africa
where Ta is often interpolated from anomalies at stations tens of kilometers
away. So just because Ts and Ta don’t always agree it doesn’t necessarily mean
Ts is wrong.
2. Much of the noise likely cancels out when
aggregating temperatures to the monthly or seasonal time scales that are used
in regressions that relate outcomes to temperature. For applications that
require finer temporal resolution of temperature measures, the noise in Ts may
become more important – although again, whether it is larger than noise in
high-temporal-resolution Ta remains an empirical question.
3. Unlike ground measurements, satellite data come
from a consistent sensor. Relative spatial variations could therefore be
captured more precisely with satellites than with ground measurements from
different instruments.
4. There is reason to believe that Ts could be more
appropriate than Ta for agricultural applications. In vegetated areas much of
the noise in the daytime Ts vs. Ta relationship arises from anomalous canopy
transpiration rates, with stressed canopies often several degrees warmer than
Ta whereas healthy canopies are typically several degrees below Ta. Thus, Ts provides
a more direct measure of crop condition than Ta, and this represents an
advantage of Ts for agricultural applications that may compensate for some of
its deficiencies.
Overall
this exercise increased our optimism that Ts can serve as a replacement for Ta
in some applications. One of the primary downsides of using satellite data is
that the records do not go back as far as ground station records so this
approach will not be appropriate for many studies with longer timescales. There
are also some issues surrounding the transformation of Ts units into Ta units
that we discuss in the paper. Despite these caveats, for studies covering
recent years our results suggest that Ts is a viable option for replacing Ta,
and that in areas with poor ground station coverage, using satellites to
measure temperature may in fact be the better
option.
