Surface Air Temperature Reconstructions Research Articles

Improvements to the Land Surface Air Temperature Reconstruction in NOAAGlobalTemp: An Artificial Neural Network Approach

Abstract NOAA global surface temperature (NOAAGlobalTemp) is NOAA’s operational global surface temperature product, which has been widely used in Earth’s climate assessment and monitoring. To improve the spatial interpolation of monthly land surface air temperatures (LSATs) in NOAAGlobalTemp from 1850 to 2020, a three-layer artificial neural network (ANN) system was designed. The ANN system was trained by repeatedly randomly selecting 90% of the LSATs from ERA5 (1950–2019) and validating with the remaining 10%. Validations show clear improvements of ANN over the original empirical orthogonal teleconnection (EOT) method: the global spatial correlation coefficient (SCC) increases from 65% to 80%, and the global root-mean-square difference (RMSD) decreases from 0.99° to 0.57°C during 1850–2020. The improvements of SCCs and RMSDs are larger in the Southern Hemisphere than in the Northern Hemisphere and are larger before the 1950s and where observations are sparse. The ANN system was finally fed in observed LSATs, and its output over the global land surface was compared with those from the EOT method. Comparisons demonstrate similar improvements by ANN over the EOT method: The global SCC increased from 78% to 89%, the global RMSD decreased from 0.93° to 0.68°C, and the LSAT variability quantified by the monthly standard deviation (STD) increases from 1.16° to 1.41°C during 1850–2020. While the SCC, RMSD, and STD at the monthly time scale have been improved, long-term trends remain largely unchanged because the low-frequency component of LSAT in ANN is identical to that in the EOT approach. Significance Statement The spatial interpolation method of an artificial neural network has greatly improved the accuracy of land surface air temperature reconstruction, which reduces root-mean-square error and increases spatial coherence and variabilities over the global land surface from 1850 to 2020.

How useful is snow accumulation in reconstructing surface air temperature in Antarctica? A study combining ice core records and climate models

Abstract. Improving our knowledge of the temporal and spatial variability of the Antarctic Ice Sheet (AIS) surface mass balance (SMB) is crucial to reduce the uncertainties of past, present, and future Antarctic contributions to sea level rise. An examination of the surface air temperature–SMB relationship in model simulations demonstrates a strong link between the two. Reconstructions based on ice cores display a weaker relationship, indicating a model–data discrepancy that may be due to model biases or to the non-climatic noise present in the records. We find that, on the regional scale, the modeled relationship between surface air temperature and SMB is often stronger than between temperature and δ18O. This suggests that SMB data can be used to reconstruct past surface air temperature. Using this finding, we assimilate isotope-enabled SMB and δ18O model output with ice core observations to generate a new surface air temperature reconstruction. Although an independent evaluation of the skill is difficult because of the short observational time series, this new reconstruction outperforms the previous reconstructions for the continental-mean temperature that were based on δ18O alone. The improvement is most significant for the East Antarctic region, where the uncertainties are particularly large. Finally, using the same data assimilation method as for the surface air temperature reconstruction, we provide a spatial SMB reconstruction for the AIS over the last 2 centuries, showing large variability in SMB trends at a regional scale, with an increase (0.82 Gt yr−2) in West Antarctica over 1957–2000 and a decrease in East Antarctica during the same period (−0.13 Gt yr−2). As expected, this is consistent with the recent reconstruction used as a constraint in the data assimilation.

The Maunder minimum and the Little Ice Age: an update from recent reconstructions and climate simulations

The Maunder minimum (MM) was a period of extremely low solar activity from approximately AD 1650 to 1715. In the solar physics literature, the MM is sometimes associated with a period of cooler global temperatures, referred to as the Little Ice Age (LIA), and thus taken as compelling evidence of a large, direct solar influence on climate. In this study, we bring together existing simulation and observational studies, particularly the most recent solar activity and paleoclimate reconstructions, to examine this relation. Using northern hemisphere surface air temperature reconstructions, the LIA can be most readily defined as an approximately 480 year period spanning AD 1440–1920, although not all of this period was notably cold. While the MM occurred within the much longer LIA period, the timing of the features are not suggestive of causation and should not, in isolation, be used as evidence of significant solar forcing of climate. Climate model simulations suggest multiple factors, particularly volcanic activity, were crucial for causing the cooler temperatures in the northern hemisphere during the LIA. A reduction in total solar irradiance likely contributed to the LIA at a level comparable to changing land use.

An assessment of particle filtering methods and nudging for climate state reconstructions

Abstract. Using the climate model of intermediate complexity LOVECLIM in an idealised framework, we assess three data-assimilation methods for reconstructing the climate state. The methods are a nudging, a particle filter with sequential importance resampling, and a nudging proposal particle filter and the test case corresponds to the climate of the high latitudes of the Southern Hemisphere during the past 150 yr. The data-assimilation methods constrain the model by pseudo-observations of surface air temperature anomalies obtained from the same model, but different initial conditions. All three data-assimilation methods provide with good estimations of surface air temperature and of sea ice concentration, with the nudging proposal particle filter obtaining the highest correlations with the pseudo-observations. When reconstructing variables that are not directly linked to the pseudo-observations such as atmospheric circulation and sea surface salinity, the particle filters have equivalent performance and their correlations are smaller than for surface air temperature reconstructions but still satisfactory for many applications. The nudging, on the contrary, obtains sea surface salinity patterns that are opposite to the pseudo-observations, which is due to a spurious impact of the nudging on vertical exchanges in the ocean.

Relationship between Holocene climate variations over southern Greenland and eastern Baffin Island and synoptic circulation pattern

Abstract. Lake pollen records from southwest Greenland and eastern Baffin Island show strong regionalism in climate trends of the last 7000 cal years. July surface air temperature reconstructions from pollen indicate larger amplitude cooling in southwest Greenland (>3.0°C) than in eastern Baffin Island (<1.0°C). This west-east gradient in climate change is consistent with August sea-surface temperature reconstructions from dinocyst records that indicate decreasing temperature and/or strength of the North Atlantic Current to the east during the Holocene while the eastern Canadian margins under the Labrador Current influence display slight warming. Complementary to air and sea-surface temperature records, the lake pollen data led to reconstruct increased cloudiness in southern Greenland, which points to increasing cyclonic activity since 7000 cal years BP west of Greenland. Together, the terrestrial and marine records of the northwest North Atlantic therefore suggest a shift from a dominant NAO+ during the early-mid Holocene to dominant NAO- in the late Holocene.

Testing a European winter surface temperature reconstruction in a surrogate climate

We evaluate the skill of a European winter surface air temperature reconstruction over the last 500 years using pseudoproxies obtained from the ECHO‐G and HadCM3 climate models. The emphasis is thereby on the effect of the reduction of available predictors back in time, an issue that has not yet been investigated in detail at continental and seasonal scale. It is found that the key factor in determining the reconstruction skill is the number of predictors and particularly their spatial distribution. However considering the usually insufficient spatial and temporal predictor availability in paleo‐reconstructions, the quality of the predictors becomes more important further back in time. Not surprisingly, the lowest reconstruction skill is found in the early period when the predictor network is reduced. Important differences between ECHO‐G and HadCM3‐based pseudoproxy reconstructions are discussed and implications for future analyses are presented.

The Spatial and Temporal Structure of ENSO Nonlinearity

The spatial structure of asymmetries in sea surface temperature (SST) and surface air temperature (SAT) between average El Niño and La Niña events is considered. It is demonstrated that in historical SST and SAT reconstructions, the anomaly spatial pattern that changes sign between El Niño and La Niña events (the “linear” signal) strongly resembles that of principal component analysis (PCA) mode 1, while that which does not change sign (the “nonlinear” signal) resembles the pattern of PCA mode 2. The linear and nonlinear patterns also strongly resemble the standard deviation and skewness fields, respectively. Furthermore, temporal subsampling of long (130 yr) SST reconstructions suggests that the magnitude of the nonlinear signal and its similarity to PCA mode 2 are functions of the strength of ENSO, as measured by the standard deviation of the PCA mode-1 time series. Finally, it is found that of several coupled general circulation models (GCMs) considered, the spatial and temporal structure of the El Niño–La Niña asymmetry is captured only by the GFDL R30 model, despite large biases in its covariance structure.