The goal of this study is to determine how H₂O and HDO measurements in water vapor can be used to detect and diagnose biases in the representation of processes controlling tropospheric humidity in atmospheric general circulation models (GCMs). We analyze a large number of isotopic data sets (four satellite, sixteen ground‐based remote‐sensing, five surface in situ and three aircraft data sets) that are sensitive to different altitudes throughout the free troposphere. Despite significant differences between data sets, we identify some observed HDO/H₂O characteristics that are robust across data sets and that can be used to evaluate models. We evaluate the isotopic GCM LMDZ, accounting for the effects of spatiotemporal sampling and instrument sensitivity. We find that LMDZ reproduces the spatial patterns in the lower and mid troposphere remarkably well. However, it underestimates the amplitude of seasonal variations in isotopic composition at all levels in the subtropics and in midlatitudes, and this bias is consistent across all data sets. LMDZ also underestimates the observed meridional isotopic gradient and the contrast between dry and convective tropical regions compared to satellite data sets. Comparison with six other isotope‐enabled GCMs from the SWING2 project shows that biases exhibited by LMDZ are common to all models. The SWING2 GCMs show a very large spread in isotopic behavior that is not obviously related to that of humidity, suggesting water vapor isotopic measurements could be used to expose model shortcomings. In a companion paper, the isotopic differences between models are interpreted in terms of biases in the representation of processes controlling humidity.

Evaluating the representation of processes controlling tropical and subtropical tropospheric relative humidity (RH) in atmospheric general circulation models (GCMs) is crucial to assess the credibility of predicted climate changes. GCMs have long exhibited a moist bias in the tropical and subtropical mid and upper troposphere, which could be due to the mis‐representation of cloud processes or of the large‐scale circulation, or to excessive diffusion during water vapor transport. The goal of this study is to use observations of the water vapor isotopic ratio to understand the cause of this bias. We compare the three‐dimensional distribution of the water vapor isotopic ratio measured from space and ground to that simulated by several versions of the isotopic GCM LMDZ. We show that the combined evaluation of RH and of the water vapor isotopic composition makes it possible to discriminate the most likely cause of RH biases. Models characterized either by an excessive vertical diffusion, an excessive convective detrainment or an underestimated in situ cloud condensation will all produce a moist bias in the free troposphere. However, only an excessive vertical diffusion can lead to a reversed seasonality of the free tropospheric isotopic composition in the subtropics compared to observations. Comparing seven isotopic GCMs suggests that the moist bias found in many GCMs in the mid and upper troposphere most frequently results from an excessive diffusion during vertical water vapor transport. This study demonstrates the added value of water vapor isotopic measurements for interpreting shortcomings in the simulation of RH by climate models.

The Aura satellite Tropospheric Emission Spectrometer (TES) instrument is capable of measuring the HDO/H₂O ratio in the lower troposphere using thermal infrared radiances between 1200 and 1350 cm⁻¹. However, direct validation of these measurements is challenging due to a lack of in situ measured vertical profiles of the HDO/H₂O ratio that are spatially and temporally co-located with the TES observations. From 11 October through 5 November 2008, we undertook a campaign to measure HDO and H₂O at the Mauna Loa observatory in Hawaii for comparison with TES observations. The Mauna Loa observatory is situated at 3.1 km above sea level or approximately 680 hPa, which is approximately the altitude where the TES HDO/H₂O observations show the most sensitivity. Another advantage of comparing in situ data from this site to estimates derived from thermal IR radiances is that the volcanic rock is heated by sunlight during the day, thus providing significant thermal contrast between the surface and atmosphere; this thermal contrast increases the sensitivity to near surface estimates of tropospheric trace gases. The objective of this inter-comparison is to better characterize a bias in the TES HDO data, which had been previously estimated to be approximately 5 % too high for a column integrated value between 850 hPa and 500 hPa. We estimate that the TES HDO profiles should be corrected downwards by approximately 4.8 % and 6.3 % for Versions 3 and 4 of the data respectively. These corrections must account for the vertical sensitivity of the TES HDO estimates. We estimate that the precision of this bias correction is approximately 1.9 %. The accuracy is driven by the corrections applied to the in situ HDO and H₂O measurements using flask data taken during the inter-comparison campaign and is estimated to be less than 1 %. Future comparisons of TES data to accurate vertical profiles of in situ measurements are needed to refine this bias estimate.

Nitrate contamination in stream water and groundwater is a serious environmental problem that arises in areas of high agricultural activities or high population density. It is therefore important to identify the source and flowpath of nitrate in water bodies. In recent decades, the dual isotope analysis (δ¹⁵N and δ¹⁸O) of nitrate has been widely applied to track contamination sources by taking advantage of the difference in nitrogen and oxygen isotope ratios for different sources. However, transformation processes of nitrogen compounds can change the isotopic composition of nitrate due to the various redox processes in the environment, which often makes it difficult to identify contaminant sources. To compensate for this, the stable water isotope of the H₂O itself can be used to interpret the complex hydrological and hydrochemical processes for the movement of nitrate contaminants. Therefore, the present study aims at understanding the fundamental background of stable water and nitrate isotope analysis, including isotope fractionation, analytical methods such as nitrate concentration from samples, instrumentation, and the typical ranges of δ¹⁵N and δ¹⁸O from various nitrate sources. In addition, we discuss hydrograph separation using the oxygen and hydrogen isotopes of water in combination with the nitrogen and oxygen isotopes of nitrate to understand the relative contributions of precipitation and groundwater to stream water. This study will assist in understanding the groundwater flowpaths as well as tracking the sources of nitrate contamination using the stable isotope analysis in combination with nitrate and water.