Mean Seasonal Cycle Research Articles

Temporal Variability in Local Air Temperature Series Shows Negative Feedback

Long daily surface air temperature series from 24 European and Asian stations are analyzed, among them the Central England temperature dataset (1772 – 2005). The mean seasonal cycle in each series is removed and correlations between the consecutive increments of various increment intervals for the anomaly series are calculated. The sample correlations appear to behave similarly for all series. A positive coefficient between the short-term (daily etc) increments rapidly changes to the negative side while the increment interval increases. The value of the coefficient saturates at the level −0.5 and stays at that level for long range of intervals from 20 to 2000 days. The feature is a testimony to the regulating role of (cumulative) negative feedback in the climate system during the last two centuries.

ENSO at 6ka and 21ka from ocean–atmosphere coupled model simulations

We analyze how the characteristics of El Nino-Southern Oscillation (ENSO) are changed in coupled ocean–atmosphere simulations of the mid-Holocene (MH) and the Last Glacial Maximum (LGM) performed as part of the Paleoclimate Modeling Intercomparison Project phase 2 (PMIP2). Comparison of the model results with present day observations show that most of the models reproduce the large scale features of the tropical Pacific like the SST gradient, the mean SST and the mean seasonal cycles. All models simulate the ENSO variability, although with different skill. Our analyses show that several relationships between El Nino amplitude and the mean state across the different control simulations are still valid for simulations of the MH and the LGM. Results for the MH show a consistent El Nino amplitude decrease. It can be related to the large scale atmospheric circulation changes. While the Northern Hemisphere receives more insolation during the summer time, the Asian summer monsoon system is strengthened which leads to the enhancement of the Walker circulation. Easterlies prevailing over the central eastern Pacific induce an equatorial upwelling that damps the El Nino development. Results are less conclusive for 21ka. Large scale dynamic competes with changes in local heat fluxes, so that model shows a wide range of responses, as it is the case in future climate projections.

The Effect of Orbital Forcing on the Mean Climate and Variability of the Tropical Pacific

Abstract Using a coupled general circulation model, the responses of the climate mean state, the annual cycle, and the El Niño–Southern Oscillation (ENSO) phenomenon to orbital changes are studied. The authors analyze a 1650-yr-long simulation with accelerated orbital forcing, representing the period from 142 000 yr b.p. (before present) to 22 900 yr a.p. (after present). The model simulation does not include the time-varying boundary conditions due to ice sheet and greenhouse gas forcing. Owing to the mean seasonal cycle of cloudiness in the off-equatorial regions, an annual mean precessional signal of temperatures is generated outside the equator. The resulting meridional SST gradient in the eastern equatorial Pacific modulates the annual mean meridional asymmetry and hence the strength of the equatorial annual cycle. In turn, changes of the equatorial annual cycle trigger abrupt changes of ENSO variability via frequency entrainment, resulting in an anticorrelation between annual cycle strength and ENSO amplitude on precessional time scales.

Circulation and water mass transformation in a model of the Chukchi Sea

The circulation and water mass transformation in a regional ocean‐ice model of the Chukchi Sea are discussed. The model has horizontal resolution of O(4 km), is forced by fluxes derived from daily NCEP reanalysis fields, and has seasonally varying transport, temperature, and salinity imposed at Bering Strait. Many of the observed characteristics of the mean circulation and seasonal cycle in the Chukchi Sea are reproduced. The discussion focuses on: the branching of the inflow transport into pathways following Herald Canyon, Central Channel, and the Alaskan coast; the pattern of ice melt; and the water mass transformation and formation of winter water and hypersaline water. The ice melt pattern and timing is strongly influenced by advection through Bering Strait. High frequency forcing results in a larger region of ice melt, particularly over the shoals and in the northern Chukchi Sea, compared to monthly mean forcing. In the model, the seasonal cycle of salinity in the southern and central Chukchi Sea is dominated by advection through Bering Strait, while local atmospheric forcing and brine rejection are more important north of Herald and Hanna Shoals and in Barrow Canyon. However, since the residence time in the Chukchi Sea is generally less than 1 year, interannual variability in the Bering Strait salinity will be reflected in the salinity across the Chukchi Sea and at Barrow Canyon.

A review of sea level observations and low frequency sea-level variability in South Atlantic

Sea-level data from South Atlantic stations were quality-controlled and analysed. Eleven time series, longer than 20 years, were selected to study long-term variability. The main features of South Atlantic sea level were described by computing linear trends, mean seasonal cycles and power spectra for the selected time series. The data sparseness made it difficult to achieve a comprehensive description of the basin. The connection between sea-level variability and ENSO was studied using the composite time series obtained by merging data for Buenos Aires and Palermo, on the Argentinian side of Rio de la Plata. It was possible to recognize that sea-level variability is strongly connected with that of river discharge into Rio de la Plata, which in turn exhibits a lagged teleconnection with ENSO. The time lag between ENSO and sea-level anomalies is about 5 months.

Impact of different convective cloud schemes on the simulation of the tropical seasonal cycle in a coupled ocean–atmosphere model

The simulation of the mean seasonal cycle of sea surface temperature (SST) remains a challenge for coupled ocean–atmosphere general circulation models (OAGCMs). Here we investigate how the numerical representation of clouds and convection affects the simulation of the seasonal variations of tropical SST. For this purpose, we compare simulations performed with two versions of the same OAGCM differing only by their convection and cloud schemes. Most of the atmospheric temperature and precipitation differences between the two simulations reflect differences found in atmosphere-alone simulations. They affect the ocean interior down to 1,000 m. Substantial differences are found between the two coupled simulations in the seasonal march of the Intertropical Convergence Zone in the eastern part of the Pacific and Atlantic basins, where the equatorial upwelling develops. The results confirm that the distribution of atmospheric convection between ocean and land during the American and African boreal summer monsoons plays a key role in maintaining a cross equatorial flow and a strong windstress along the equator, and thereby the equatorial upwelling. Feedbacks between convection, large-scale circulation, SST and clouds are highlighted from the differences between the two simulations. In one case, these feedbacks maintain the ITCZ in a quite realistic position, whereas in the other case the ITCZ is located too far south close to the equator.

Interaction between Near-Annual and ENSO Modes in a CGCM Simulation: Role of the Equatorial Background Mean State

Abstract A 260-yr-long coupled general circulation model (CGCM) simulation is used to investigate the interaction between ENSO mode and near-annual variability and its sensitivity to the equatorial background mean stratification and seasonal cycles. Although the thermocline mean vertical structure of the model favors the high-order baroclinic modes that are associated with the slow time scales of the coupled variability, the simulated ENSO oscillates at a dominant quasi-biennial frequency. Biases of the climatological velocity field are favorable to the dominance of the zonal advective feedback over the thermocline feedback, the model exhibiting an overenergetic westward seasonal zonal current in the central-western equatorial Pacific, and an upwelling rate that is about half the observations. This sets the conditions for the enhancement of a near-annual mode that is observed to oscillate at an 8-month period in the model. Using an intermediate coupled model of the tropical Pacific where the climatological fields are prescribed to the ones derived from the CGCM, it is demonstrated that the quasi-biennial ENSO variability simulated by the CGCM is mostly due to the biases in the climatological currents of the CGCM. These biases favor the dominance of the fast “zonal advective feedback” over the slow “thermocline feedback” in the coupled system and enhance a fast coupled basin mode. This fast mode differs from the theoretical Pacific Ocean basin mode in that, besides mean temperature advection by the zonal current anomalies, it is also driven by anomalous temperature advection by the total current. Results suggest that the near-annual mode destabilizes the ENSO mode to produce overenergetic quasi-biennial oscillations in the model. It also contributes to the ENSO asymmetry and the cold bias of the CGCM mean state by nonlinear accumulation of temperature zonal advection, which works toward the cold in the western Pacific more than the warm in the east. It is suggested that the model equilibrium results from the interaction between the ENSO mode, the near-annual mode, and the mean state.

Contribution of land‐atmosphere coupling to recent European summer heat waves

Most of the recent European summer heat waves have been preceded by a pronounced spring precipitation deficit. The lack of precipitation and the associated depletion of soil moisture result in reduced latent cooling and thereby amplify the summer temperature extremes. In order to quantify the contribution of land‐atmosphere interactions, we conduct regional climate simulations with and without land‐atmosphere coupling for four selected major summer heat waves in 1976, 1994, 2003, and 2005. The coupled simulation uses a fully coupled land‐surface model, while in the uncoupled simulation the mean seasonal cycle of soil moisture is prescribed. The experiments reveal that land‐atmosphere coupling plays an important role for the evolution of the investigated heat waves both through local and remote effects. During all simulated events soil moisture‐temperature interactions increase the heat wave duration and account for typically 50–80% of the number of hot summer days. The largest impact is found for daily maximum temperatures during heat wave episodes.

Mean seasonal cycle of isothermal depth in the South China Sea

The mean seasonal cycle of isothermal depth is examined using all available temperature profiles in the South China Sea. On the annual average, the isothermal depth has two deep cores (>45 m). One extends westward from the Luzon Strait along the continental slope south of China, and the other in the deep basin of the southern South China Sea. Harmonic analysis shows that the seasonal variation of isothermal depth is predominantly annual in the northern South China Sea, exceeding 70 m in winter and falling below 20 m in summer near the continental slope south of China. The annual variation is weaker in the southern South China Sea, where the isothermal depth approaches its seasonal maximum (>55 m) in fall and minimum (<35 m) in spring. The semiannual variation is most prominent in the southern South China Sea, being of comparable strength with the annual variation. Among others, local Ekman pumping appears to be an important process responsible for the semiannual variation in the southern South China Sea. A detailed description of the month‐to‐month variations is provided, to set a basic background for further understanding the South China Sea mixed layer dynamics and thermodynamics.

The Impact of Cloud Radiative Feedback, Remote ENSO Forcing, and Entrainment on the Persistence of North Pacific Sea Surface Temperature Anomalies

Abstract The influence of cloud radiative feedback, remote ENSO heat flux forcing, and oceanic entrainment on persisting North Pacific sea surface temperature (SST) anomalies is investigated using a stochastically forced ocean mixed layer model. The stochastic heat flux is estimated from an atmospheric general circulation model, the seasonally varying radiative feedback parameter and remote ENSO forcing are obtained from observations, and entrainment is derived from the observed mean seasonal cycle of ocean mixed layer depth. Persistence is examined via SST autocorrelations in the western, central, and subtropical eastern North Pacific and for the leading pattern of variability across the basin. The contribution of clouds, ENSO, and entrainment to SST persistence is evaluated by comparing simulations with and without each term. The SST autocorrelation structure in the model closely resembles nature: the pattern correlation between the two is 0.87–0.9 in the three regions and for the basinwide analyses, and 0.35–0.66 after subtracting an exponential function representing the background damping resulting from air–sea heat fluxes. Positive radiative feedback enhances SST autocorrelations (∼0.1–0.3) from late spring to summer in the central and western Pacific and from late summer to fall in the subtropical eastern Pacific. The influence of the remote ENSO forcing on SST autocorrelation varies with season and location with a maximum impact on the correlation magnitude of 0.2–0.3. The winter-to-winter recurrence of higher autocorrelations is caused by entrainment, which generally suppresses SST variability but returns thermal anomalies sequestered beneath the mixed layer in summer back to the surface in the following fall/winter. This reemergence mechanism enhances SST autocorrelation by ∼0.3 at lags of 9–12 months from the previous winter in the western and central Pacific, but only slightly enhances autocorrelation (∼0.1) in the subtropical eastern Pacific. The impact of clouds, ENSO, and entrainment on the autocorrelation structure of the basinwide SST anomaly pattern is similar to that in the western region. ENSO’s impact on the basinwide North Pacific SST autocorrelation in an atmospheric general circulation model coupled to an ocean mixed layer model with observed SSTs specified in the tropical Pacific is very similar to the results from the stochastic model developed here.

Tropical Atlantic SST Prediction with Coupled Ocean–Atmosphere GCMs

Abstract Variations in tropical Atlantic SST are an important factor in seasonal forecasts in the region and beyond. An analysis is given of the capabilities of the latest generation of coupled GCM seasonal forecast systems to predict tropical Atlantic SST anomalies. Skill above that of persistence is demonstrated in both the northern tropical and equatorial Atlantic, but not farther south. The inability of the coupled models to correctly represent the mean seasonal cycle is a major problem in attempts to forecast equatorial SST anomalies in the boreal summer. Even when forced with observed SST, atmosphere models have significant failings in this area. The quality of ocean initial conditions for coupled model forecasts is also a cause for concern, and the adequacy of the near-equatorial ocean observing system is in doubt. A multimodel approach improves forecast skill only modestly, and large errors remain in the southern tropical Atlantic. There is still much scope for improving forecasts of tropical Atlantic SST.

Biological control of the vernal population increase of Calanus finmarchicus on Georges Bank

Abstract An adjoint data assimilation approach was used to quantify the physical and biological controls on Calanus finmarchicus N3–C6 stages on Georges Bank and its nearby environs. The mean seasonal cycle of vertically averaged distributions, from 5 years of the GLOBEC Georges Bank Broad-Scale Surveys between January and June, was assimilated into a physical–biological model based on the climatological circulation. Large seasonal and spatial variability is present in the inferred supply sources, mortality rates, computed molting fluxes, and physical transports. Estimated mortalities fall within the range of observed rates, and exhibit stage structure that is consistent with earlier findings. Inferred off-bank initial conditions indicate that the deep basins in the Gulf of Maine are source regions of early stage nauplii and late-stage copepodids in January. However, the population increase on Georges Bank from January to April is controlled mostly by local biological processes. Magnitudes of the physical transport terms are nearly as large as the mortality and molting fluxes, but their bank-wide averages are small in comparison to the biological terms. The hypothesis of local biological control is tested in a sensitivity experiment in which upstream sources are set to zero. In that solution, the lack of upstream sources is compensated by a decrease in mortality that is much smaller than the uncertainty in observational estimates.

Physical mechanisms of the Australian summer monsoon: 2. Variability of strength and onset and termination times

Variability of the Australian monsoon onset, termination, and strength was investigated in terms of the first five major modes of monsoon precipitation found via cyclostationary empirical orthogonal function analysis. They are the El Niño–Southern Oscillation (ENSO) mode, the seasonal cycle, the ENSO transition mode, and the two Madden‐Julian oscillation (MJO) modes, respectively. Whereas the seasonal cycle defines the fixed onset (∼5 January) and termination (∼5 March) dates, the presence of the other modes alters these dates. The impact of each mode was investigated after each mode, with a strength corresponding to 1 standard deviation of its variability, was added to or subtracted from the mean seasonal cycle. It is shown that the contribution of each mode to the monsoon precipitation is geographically complex and varies significantly throughout the monsoon period. One striking feature is that the impact of each mode is highly asymmetric with respect to its phase. The negative modes generally affect more significantly the onset, the termination, and the amount of monsoon precipitation. Although each of the four modes makes a unique and tangible contribution, the ENSO mode contributes most significantly to the overall mean and variance of the monsoon precipitation variability. While the positive ENSO (El Niño) mode does not seriously alter the onset and termination times, the negative ENSO (La Niña) mode prolongs the duration of the Australian summer monsoon significantly. Although the contribution to the overall mean is small, the two MJO modes are the most dominating factor controlling the onset and termination times of the Australian summer monsoon.

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Spatial patterns in seasonal and interannual variability of chlorophyll and sea surface temperature in the California Current

Physical forcing and biological response within the California Current System (CCS) are highly variable over a wide range of scales. Satellite remote sensing offers the only feasible means of quantifying this variability over the full extent of the CCS. Using six years (1997–2003) of daily SST and chlorophyll imagery, we map the spatial dependence of dominant temporal variability at resolutions sufficient to identify recurrent mesoscale circulation and local pattern associated with coastal topography. Here we describe mean seasonal cycles and interannual variation; intraseasonal variability is left to a companion paper (K. R. Legaard and A. C. Thomas, manuscript in preparation, 2006). Coastal upwelling dictates seasonality along north‐central California, where weak cycles of SST fluctuate between spring minima and late summer maxima and chlorophyll peaks in early summer. Off northern California, chlorophyll maxima are bounded offshore by the seasonally recurrent upwelling jet. Seasonal cycles differ across higher latitudes and in the midlatitude Southern California Bight, where upwelling winds are less vigorous and/or persistent. Seasonality along south‐central Baja is strongly affected by processes other than upwelling, despite year‐round upwelling‐favorable winds. Interannual variation is generally dominated by El Niño and La Niña conditions. Interannual SST variance is greatest along south‐central Baja, although interannual variability constitutes a greater fraction of total variance inshore along southern Oregon and much of California. Patterns of interannual chlorophyll variance are consistent with dominant forcing through the widespread depression and elevation of the nutricline during El Niño and La Niña, respectively. Interannual variability constitutes a greater fraction of total chlorophyll variance offshore.

Comparing 20 years of precipitation estimates from different sources over the world ocean

The paper compares ten different global precipitation data sets over the oceans and discusses their respective strengths and weaknesses in ocean regions where they are potentially important to the salinity and buoyancy budgets of surface waters. Data sets (acronyms of which are given in Section 2) are categorised according to their source of data, which are (1) in situ for Center for Climatic Research (Legates and Willmott, 1990; Archive of Precipitation Version 3.01, http://climate.geog.udel.edu/~climate), Southampton Oceanography Centre (SOC) (Josey et al., J Clim 12:2856–2880, 1999) and University of Wisconsin-Milwaukee (UWM) (Da Silva et al. 1994); (2) satellite for Microwave Sounding Unit (MSU) (Spencer, J Clim 6:1301–1326, 1993), TOPEX (Quartly et al., J Geophys Res 104:31489–31516, 1999), and Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS) (Bauer and Schluessel, J Geophys Res 98:20737–20759, 1993); (3) atmospheric forecast model re-analyses for European Centre for Medium-range Weather Forecast (ECMWF) (Gibson et al. 1997) and National Center for Environmental Prediction (NCEP) (Kalnay et al., Bull Am Meteorol Soc 77:437–471, 1996); and (4) composite for Global Precipitation Climatology Project (GPCP) (satellites and rain gauges, Huffman et al., Bull Am Meteorol Soc 78(1):5–20, 1997) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) (satellites, rain gauges and atmospheric forecast model, Xie and Arkin, Bull Am Meteorol Soc 78(11):2539–2558, 1997). Although there is no absolute field of reference, composite data sets are often considered as the best estimates. First, a qualitative comparison is carried out, which provides for each data set, a description of the geographical distribution of the climatological mean precipitation field. A more careful comparison between data sets is undertaken over periods they have in common. First, six among the ten data sets (SOC, UWM, ECMWF, NCEP, MSU and CMAP) are compared over their common period of 14 years, from 1980 to 1993. Then CMAP is compared to GPCP over the 1988–1995 period and to HOAPS over the 1992–1998 period. Usual diagnostics, like comparison of the precipitation patterns exhibited in the annual climatological means of zonal averages and global budget, are used to investigate differences between the various precipitation fields. In addition, precipitation rates are spatially integrated over 16 regional boxes, which are representative of the major ocean gyres or large-scale ocean circulation patterns. Seasonal and inter-annual variations are studied over these boxes in terms of time series anomalies or correlation coefficients. The analysis attempts to characterise differences and biases according to the original source of data (i.e. in situ or satellite, etc.). Qualitative agreement can be observed in all climatologies, which reproduce the major characteristics of the precipitation patterns over the oceans. However, great disagreements occur in terms of quantitative values and regional patterns, especially in regions of high precipitation. However, a better agreement is generally found in the northern hemisphere. The most significant differences, observed between data sets in the mean seasonal cycles and interannual variations, are discussed. A major result of the paper, which was not expected a priori, is that differences between data sets are much more dependent upon the ocean region that is considered than upon the origin of the data sets (in situ vs satellite vs model, etc.). Our analysis did not provide enough objective elements, which would allow us to clearly recommend a given data set as reference or best estimate. However, composite data sets (GPCP, and especially CMAP), because they never appeared to be really “off” when compared to other data sets, may represent the best recent data set available. CMAP would certainly be our first choice to drive an ocean GCM.

Observing seasonal bottom pressure variability in the North Pacific with GRACE

Using monthly gravity field solutions from GRACE together with output from an ocean model we assess the ability of GRACE to observe seasonal ocean bottom pressure variability in the North Pacific. First, the spatial structure and temporal evolution of the long‐term mean seasonal bottom pressure cycle in the North Pacific is characterised in the context of the ocean model. It is then shown that a mean seasonal cycle based on a limited subset of the model output corresponding to the intervals over which the GRACE monthly solutions were formed is representative of this long‐term seasonal cycle. It is therefore concluded that the significant features of the seasonal cycle should be present in the GRACE data. Agreement is found between the ocean model and GRACE estimates of the bottom pressure seasonal cycle, thus demonstrating the worth of GRACE as a tool for studying ocean dynamics.

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America

Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Panel on Climate Change Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three Special Report on Emissions Scenarios (SRES) A2, A1B, and B1. We developed a statistical method based on neural networks and Bayesian statistics to evaluate the models’ skills in simulating late twentieth century temperature over continental areas. Some criteria [model weight indices (MWIs)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. As the study demonstrates, the use of neural networks, optimized by Bayesian statistics, leads to two major results. First, the MWIs can be interpreted as optimal weights for a linear combination of the climate models. Second, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. Model simulations in the southern tip of South America and along the Chilean and Peruvian coasts or in the northern coasts of South America (Venezuela, Guiana) are particularly poor. Overall, our results present an upper bound of potential temperature warming for each scenario. Spatially, in SRES A2, our major findings are that Tropical South America could warm up by about 4°C, while southern South America (SSA) would also undergo a near 2–3°C average warming. Interestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. In SSA, the amplitude of the seasonal cycle tends to increase, while in northern South America, the amplitude of the seasonal cycle would be reduced leading to much milder winters. We show that all the scenarios have similar patterns and only differ in amplitude. SRES A1B differ from SRES A2 mainly for the late twenty-first century, reaching more or less an 80–90% amplitude compared to SRES A2. SRES B1, however, diverges from the other scenarios as soon as 2025. For the late twenty-first century, SRES B1 displays amplitudes, which are about half those of SRES A2.

AGCM Biases in Evaporation Regime: Impacts on Soil Moisture Memory and Land–Atmosphere Feedback

Abstract Because precipitation and net radiation in an atmospheric general circulation model (AGCM) are typically biased relative to observations, the simulated evaporative regime of a region may be biased, with consequent negative effects on the AGCM’s ability to translate an initialized soil moisture anomaly into an improved seasonal prediction. These potential problems are investigated through extensive offline analyses with the Mosaic land surface model (LSM). The LSM was first forced globally with a 15-yr observation-based dataset. The simulation was then repeated after imposing a representative set of GCM climate biases onto the forcings—the observational forcings were scaled so that their mean seasonal cycles matched those simulated by the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1; NASA Global Modeling and Assimilation Office) AGCM over the same period. The AGCM’s climate biases do indeed lead to significant biases in evaporative regime in certain regions, with the expected impacts on soil moisture memory time scales. Furthermore, the offline simulations suggest that the biased forcing in the AGCM should contribute to overestimated feedback in certain parts of North America—parts already identified in previous studies as having excessive feedback. The present study thus supports the notion that the reduction of climate biases in the AGCM will lead to more appropriate translations of soil moisture initialization into seasonal prediction skill.

Evaluation of Soil Moisture in the NCEP–NCAR and NCEP–DOE Global Reanalyses

Abstract This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.