dataesr/scientific-paragraphs-categorization huggingface dataset:
The four retrieval algorithms differ for some assumptions in the forward model, for the spectral intervals used for the analysis and for the retrieval approach, and a profitable cross-fertilization has occurred among them. Studies have been performed for the FR measurements proving that in general consistent results for the operational species are obtained by the different algorithms (see von  Clarmann et al. , 2003a; Fischer et al. , 2008) , while studies on the OR measurements are currently ongoing and will be reported in later publications. Other retrieval schemes, used principally for the retrieval of CFCs from MIPAS measurements, are OPERA  (Moore and Remedios, 2008)  and JURASSIC  (Hoffmann et al. , 2008) , which use optimal estimation on spectrally integrated radiances. This paper is focused on the ESA processor that was used during the Envisat mission for both the near real time analysis, whose requirement is the distribution of the products within three hours from the measurement time, and for the offline analysis, performed on consolidated Level 1 data, that benefits from a posteriori knowledge related to calibration, auxiliary data and precise orbit information.
The capacity of operating in near real time made the MIPAS ESA dataset particularly suitable for the use in operational data assimilation systems. Indeed, near real time MIPAS ozone profiles were actively assimilated at the ECMWF (European Centre for Medium-Range Weather Forecasts) from October 2003 until the end of March 2004  (Dethof, 2003) , and, after the interruption in 2004, from 8 December 2011 until 8 April 2012. The assimilation of MIPAS ozone data at the ECMWF was proven to substantially improve the quality of the ECMWF ozone analyses  (Dragani, 2012; Thépaut et al. , 2012) , in particular the good vertical resolution of MIPAS in the stratosphere could provide a very useful constraint on the ozone analyses. The description and characterization of the ESA processor's products of the full resolution phase was already described in  Ridolfi et al.
(2000)  and  Raspollini et al. (2006). In this paper we focus on the performances of the latest version of the ESA processor ML2PP (MIPAS Level 2 Prototype Processor) V6, which has been used for reprocessing the entire MIPAS mission, i. e. , including both phases. The complete ML2PP V6 database has been released in 2012  (ESA, 2012b) , data access is provided via fast registration at https: //earth. esa. int/web/guest/data-access/browse-data-products. The ML2PP V6 dataset contains the upgrades that were needed after the change of the operating mode in 2005 both in the algorithm, to cope with the finer altitude sampling of the OR measurements, and in the spectral intervals selected for each retrieval, to cope with the reduced spectral resolution. Since the retrieval is performed at the grid of the measured tangent altitudes and this grid is significantly finer, for the OR measurements, than the MIPAS instantaneous field of view (IFOV, 3 km) in the upper troposphere and lower stratosphere, the retrieval of the OR measurements resulted in an ill-conditioned problem and required a regularization approach that was not previously needed for the analysis of the FR measurements.
Characteristics of the two phases of the MIPAS mission In both phases of the mission different measurement modes are used to observe different processes in the atmosphere with either better sampling or better altitude coverage. Corresponding modes in the two phases of the mission are meant to achieve the same scientific objectives but may slightly differ for the altitude coverage and for the altitude grid. The general objectives of the different measurement modes are shortly reviewed. The nominal mode (NOM) is the basic mode used to study chemistry and transport and to provide a database for climatology, trend analyses and near real time applications. It is designed to cover the upper troposphere, the stratosphere and the lower mesosphere (approximated boundaries 6-70 km). The upper troposphere-lower stratosphere (UT-LS) measurement mode, called UTLS-1, is tailored to studies of atmospheric processes in the UT-LS region (including the tropical tropopause layer).
For a detailed description of all measurement modes used in the two phases of the mission see also  Oelhaf (2008) , while for a detailed calendar of MIPAS measurements see  Dudhia (2008). The MIPAS ML2PP V6 dataset covers all described measurement modes, although MA and UA modes are analyzed by the ESA processor only up to 70 km, since above this altitude the impact of non-LTE cannot be neglected and non-LTE effects are not handled in the forward model of the ESA processor. Analysis of MA and UA modes on the full altitude range are performed with the IMK-IAA algorithm  (García-Comas et al. , 2012; Bermejo-Pantaleon et al. , 2011) , while the analysis of UTLS-1 mode with IMK processor is described in  Chauhan et al. (2009).
Upgrades in the auxiliary data In addition to the changes in the algorithm, the analysis of the OR measurements required also new auxiliary data. The main change was made in the MW database, i. e. , the database of the spectral intervals selected for the retrieval containing the best information on the target species and being less affected by systematic errors. The MW selection is based on the minimization of the total error, including both random and systematic errors (see Sect. 4. 1). The change of the spectral resolution modifies the contribution of each of these errors and leads to a different optimized selection of MWs in the two phases of the mission. Furthermore, the MWs for the analysis of the OR measurements were selected using some modified estimation of these errors, according to the improved knowledge on some MIPAS instrument errors as described in Sect.
The impact of this discontinuity on the retrieval performances is investigated in terms of random and systematic error and spatial resolution in the subsequent sections, while the possible presence of bias between the two phases of the mission due to the different systematic errors affecting the two sets of MWs and to the different measurement scenario will be investigated in a subsequent companion paper  (Raspollini et al. , 2013). Further changes in the auxiliary data involve the MI-PAS dedicated spectroscopic database. ML2PP V6 uses V3. 2 of the spectroscopic database. With respect to V3. 1 described in  Raspollini et al. (2006) , V3. 2 includes updates in NH 3 , OCS, NO, H 2 O 2 , HNO 3 , as well as updated pressure broadening (and its temperature dependence) of H 2 O at 948. 26288 cm -1.
Altitude bias The MIPAS ESA Level 2 processor retrieves the pressure at the tangent levels and the temperature corresponding to this pressure. The spacing between two consecutive tangent altitudes is determined from these quantities using the hydrostatic equilibrium law constrained by the engineering information on the distance between two contiguous tangent altitudes. The location of the altitude grid is determined using the lowest tangent altitude, assumed to be equal to the corresponding engineering value, as a reference point. In this way the tangent altitudes retrieved by the Level 2 processor are affected by a constant bias equal to the error of the engineering measurement at the lowest altitude, which may reach peak values as large as 1. 5 km. No correction of this bias is made in ML2PP V6, but the potential bias in the lowest retrieved tangent altitude has been estimated with a method that takes into account the height and pressure relation provided by the ECMWF atmospheric model high resolution 10 day forecast data  (ECMWF, 2009).
Conclusions With almost ten years of measurements, MIPAS products constitute an extended and valuable dataset, important for studies of trends, climatology, and in general for the study of the physics and chemistry of the atmosphere. The MI-PAS mission is divided in two phases: the so-called FR phase, in the first two years of operation (from July 2002 to March 2004) when the instrument acquired, nearly continuously, measurements at full spectral resolution (sampled at 0. 025 cm -1 ); and the so-called OR phase (from January 2005 to April 2012), in which the instrument operated with a reduced spectral resolution (0. 0625 cm -1 ), but improved spatial resolution, that is with more frequent observations both in the vertical (limb) domain and in the horizontal domain (along the orbit).
On the contrary, thanks to the use of a self-adapting regularization schemes in the retrieval algorithm, a small variation of the vertical resolution is obtained for different atmospheric conditions. The minimal use of external constraints, which are prone to introduce biases in the solution, makes ESA processor products suitable for the climatological studies. The use of different MWs in the two phases and in general the change in the measurement scenario impose some caution when using the full ten year dataset to extract trends. This issue will be further investigated in a subsequent companion paper  (Raspollini et al. , 2013). Supplementary material related to this article is available online at http://www. atmos-meas-tech. net/6/ 2419/2013/amt-6-2419-2013-supplement. pdf.
Acknowledgements. This work has been performed under the  ESA  study "Support to MIPAS Level 2 product validation", contract  ESA-ESRIN  no. 21719/08/I-OL. The authors are grateful to the  Astrium  team that developed the industrial prototype ML2PP using the ORM code as reference, to  Thorsten Fehr  and  Rolf von Kulhmann  for the coordination of the MIPAS Quality Working Group activities and to  Michael Kiefer  for the work done within the MIPAS Quality Working Group. Edited by:  A. Lambert.
Data collection: APEX2 (Bruker, 2007); cell refinement: SAINT (Bruker, 2007); data reduction: SAINT (Bruker, 2007);program(s) used to solve structure: SHELXS97 (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL2013 (Sheldrick, 2008) ; molecular graphics: DIAMOND (Brandenburg, 1999) ; software used to prepare material for publication: publCIF (Westrip, 2010).
This approach is based on the somewhat linear dependence of stratospheric ozone changes on CO 2 changes, which has been found in coupled chemistry models run under the RCP scenarios  (Eyring et al. 2010a, b). The INCA (tropospheric) and REPROBUS (stratospheric) ozone fields are then merged with a transition region centered on the tropopause region and averaged over longitudes to produce time-varying zonally-averaged monthlymean ozone fields. Figure  2  shows the total column ozone as a function of latitude and time, from 1960 to 2100, for RCP 2. 6 and RCP 6. 0 scenarios, as well as for the ACC/SPARC ozone dataset, which is the commonly used ozone climatology in CMIP5  (Cionni et al. 2011; Eyring et al. 2012). The time evolutions of the globally-averaged total column ozone in the RCP 2. 6, 4.
5, 6. 0 and 8. 5 scenarios and in the ACC/ SPARC climatology are shown on Fig. 3. The evolutions of column ozone as a function of latitude and time are similar in our CMIP5 climatologies and in ACC/SPARC climatology. From 1960 onwards, column ozone decreases at all latitudes with smaller trends over the tropics and largest trends over Antarctica. This evolution is mostly due to the increase in ODSs (Ozone Depleting Substances) until the end of the twentieth century. The pre-2000 ozone decrease is followed by an increase with a rate that depends on the RCP scenario and on the region. There are three main differences between our CMIP5 ozone forcings and the ACC/SPARC dataset. First, the Antarctic ozone hole is more pronounced in our dataset than in the ACC/SPARC dataset. Second, although the decrease in column ozone is stronger over Antarctica in our dataset, the decline in global ozone during the end of the last century is weaker (Fig.
3 ) indicating that the past tropical column ozone declines less quickly in our climatology. Third, the values of column ozone are generally higher in our dataset. Globally-averaged total column ozone is about 10-18 DU higher in our RCP 6. 0 climatology than in the ACC/ SPARC climatology (Fig. 3 ). The faster the growth in GHG emissions (increasing from RCP 2. 6 to RCP 8. 5), the stronger the rate of ozone increase is during the twenty-first century in our forcings. By 2030 or 2040, depending on the RCP scenario, the 1960 levels in global column ozone are reached in all forcings (Fig. 3 ). However from 2040 onward, the global ozone levels off in RCP 2. 6, continues to increase slightly in RCP 4. 5 and RCP 6. 0 and increase quite sharply in RCP 8. 5. The ozone super-recovery (i. e. ozone levels exceeding the 1960s levels in the late twentyfirst century) is most visible at mid-latitudes and at northern high latitudes.
e. offline) concentration-driven simulations from 1850 to 2300, CO 2 being well mixed in the atmosphere, the prescribed global CO 2 concentration is directly used by LMDZ to compute the radiative budget and by the PISCES and ORCHIDEE models to compute air-sea CO 2 exchange and land photosynthesis respectively. The prescribed evolution of CO 2 concentrations is taken from the CMIP5 recommended dataset and is described in  Meinshausen et al. (2011). For the historical period 1850-2005, the CO 2 concentration has been derived from the Law Dome ice core record, the SIO Mauna Loa record and the NOAA global-mean record. From 2006 and onwards, CO 2 emissions have been projected by four different Integrated Assessment Models (IAMs)  (van Vuuren et al. 2011) , and corresponding CO 2 concentrations have been generated with the same reduced-complexity carbon cycle-climate model MAGICC6  (Meinshausen et al.
Other greenhouse gas concentrations Other greenhouse gases (apart from ozone) are assumed to be well mixed in the atmosphere and are prescribed as time series of annual global mean mixing ratio. The concentrations of CH 4 ,N 2 O, CFC-11 and CFC-12 are directly prescribed in the radiative code of LMDZ. The concentrations are taken from the recommended CMIP5 dataset 1  and are described in  Meinshausen et al. (2011). As the radiative schemes of GCMs do not generally represent separately all the fluorinated gases emitted by human activities, the radiative effects of all fluorinated gases controlled under the Montreal and Kyoto protocols are represented in terms of concentrations of ''equivalent CFC-12'' and ''equivalent HFC-134a''respectively. The ''equivalent CFC-12'' concentration is directly used in LMDZ whereas the ''equivalent HFC-134a'' is converted in ''equivalent CFC-11'' prior to being used.
Land use changes We use the transient historical and future crop and pasture datasets developed by  Hurtt et al. (2011)  (hereafter referred to as the UNH dataset) for both the historical period and the 4 RCPs scenarios for the future period. All the information is provided on 0. 5°9 0. 5°horizontal grid. Those datasets provide information on human activities (crop land and grazed pastureland) in each grid-cell but do not provide specific information on the characteristics of the natural vegetation. Moreover, the information provided cannot be directly used by land surface models embedded within GCMs like ORCHIDEE. The land-cover map used for both the historical and future period has been obtained starting from an observed present-day land-cover map  (Loveland et al. 2000) , which already includes both natural and anthropogenic vegetation types with the following methodology.
Firstly, the area covered by crops per year and per gridcell is set to the value provided by the UNH dataset. The expansion of this crop area occurs at the expense of all natural vegetation types proportionally. This means that the percent by which natural grasses and tree areas are reduced is the same for all biomes/PFTs. Conversely, a reduction of anthropogenic area implies a proportional increase in all natural vegetation types which exist in any given grid-cell. If no information is available on the natural distribution of vegetation at a specific location (i. e. 100 % anthropogenic on the original land-cover map used), the nearest point which has natural vegetation is searched and this vegetation is introduced. Finally, the extent covered by desert in each grid-cell is unchanged from pre-industrial times until the end of the twenty-first century.
Initial state and control run The initial state of the IPSL-CM5A-LR model was obtained in four steps. First, a 2,500-year long simulation of the oceanic model without carbon cycle where the atmospheric conditions are imposed and correspond to the version 2 of the Coordinated Ocean-ice Reference Experiments data sets  (Large and Yeager 2009)  was achieved. Second, the full carbon-cycle configuration of the IPSL-CM5A-LR model was integrated for a period of 600 years with the solar constant and the concentrations of GHGs and aerosols corresponding to their pre-industrial values. Third, because this last simulation is too short for the ocean and biosphere carbon pools to reach equilibrium, offline simulations a few thousand year-long with the ocean and land carbon cycle models (ORCHIDEE and PISCES) were conducted separately.
After a 300-year long run with the full carboncycle configuration of IPSL-CM5A-MR, only the carbon cycle over land was not in equilibrium. A few thousand year long offline simulation with the land carbon cycle model was performed to bring the biosphere carbon pools to equilibrium. Finally the complete IPSL-CM5A-MR model was integrated again for another 200 years to obtain the initial state of the control simulation. The initial state of IPSL-CM5B-LR was obtained starting from the initial state of IPSL-CM5A-LR control run and by performing a 280-year long simulation. Although the full carbon-cycle configuration is used in IPSL-CM5B-LR, this spin-up period is not long enough for the carbon pools to reach an equilibrium. The carbon variables are therefore not relevant for this model version. They have not been made available on the CMIP5 data base and will not be discussed in this paper.
Twentieth century temperature Figure  6a  displays the time evolution of the global mean air surface temperature from observations (Hadcrut3v dataset,  Jones et al. 1999; Hadcrut3v dataset, Brohan et al. 2006 ) and simulated by the IPSL-CM4 which participated in CMIP3, the IPSL-CM5A-LR, the IPSL-CM5A-MR, and the IPSL-CM5B-LR models. On this figure, the IPSL-CM5A and IPSL-CM5B simulations include all the anthropogenic and natural forcings as described in Sect. 3 whereas the IPSL-CM4 simulation only includes the GHGs and sulfate aerosol forcings with no natural forcing  (Dufresne et al. 2005). As expected all the historical simulations indicate a substantial global warming induced by increased greenhouse gas concentrations in the atmosphere. For all models the global trend and multi-annual variability agree rather well with observations but the warming trend simulated during recent decades (e.
g. from 1960 onwards) by most of the model configurations seems exaggerated. To extract the temperature trends more accurately, the monthly temperature time series from the simulations and from the observations were subjected to the STL (Seasonal-Trend decomposition procedure based on Loess) additive scheme, which is a powerful statistical technique for describing a time series  (Cleveland et al. 1990 ). The STL is a filtering procedure where the analyzed X(t) monthly time series is decomposed into three terms: XðtÞ ¼ TðtÞ þ AðtÞ þ RðtÞ ð 2Þ The T(t) term quantifies the trend and low-frequency variations in the time series. The A(t) term describes the annual cycle and its modulation through time. Finally the R(t) term contains the interannual signal and the noise present in the data. As demonstrated by  Morissey (1990)  or  Terray (2011) , this procedure is particularly useful to extract the interannual and trend signals from nonstationary and noisy climate datasets.
However IPSL-CM5A simulates a larger temperature increase than IPSL-CM4 after 1970 compared to observations although both models have a similar climate sensitivity (Sect. 6. 1). During this period the difference is probably due to the changes in (a) (b) Fig. 6  a Time evolution of the global mean air surface temperature anomaly as observed (Hadcrut3v dataset, black) and simulated by the IPSL-CM5A-LR (light blue), the IPSL-CM5A-MR (blue), the IPSL-CM5B-LR (magenta) and the IPSL-CM4 (green) models. The temperatures are smoothed using a 5-year Hanning filter b Trends of the same variable estimated from the global area-averaged temperature anomalies monthly time series as defined by the STL procedure (see text). The unit is K and the temperature anomalies are computed with respect to the 1961-1990 period.
The IPSL-CM5B-LR model has a much smaller climate sensitivity than the other model versions as will be shown in Sect. 6. 1 and this is probably the main reason for this smaller temperature increase. Compared to the observed temperature (Hadcrut3v dataset,  Jones et al. 1999; Hadcrut3v dataset, Brohan et al. 2006 ) over the period 1961-1990, the models have the following biases on average: -0. 7 K for IPSL-CM4, -1. 4 K for IPSL-CM5A-LR, -0. 4 K for IPSL-CM5A-MR and -0. 6 K for IPSL-CM5B-LR. The geographical structure of the temperature bias shows common patterns for IPSL-CM4, IPSL-CM5A-LR and IPSL-CM5A-MR. The amplitude of these biases is weakest in IPSL-CM5A-MR (Fig. 7 ), it is slightly stronger in IPSL-CM5A-LR and it is significantly stronger in IPSL-CM4. In the Pacific and Atlantic tropical oceans there is a systematic bias with the eastern part of the ocean basins being too warm compared to the western part, which is a common weakness of coupled models.
Tropical mean precipitation Figure  8  presents the 10-year (1990-1999) annual mean rainfall from GPCP (Global Precipitation Climatology Dataset) observations  (Huffman et al. 2001 ) and for historical simulations with the four versions of the IPSL model (IPSL-CM4, IPSL-CM5A-LR, IPSL-CM5A-MR and IPSL-CM5B-LR). The precipitation pattern is similar for all model versions, which are able to qualitatively reproduce the main observed structures. The same major biases are present in all model configurations. In the tropics the models show the so-called double Intertropical Convergence Zone (ITCZ) structure with a first realistic precipitation maximum around 5°N and a secondary maximum around 5°S, which is not observed. The monsoon rainfall over West Africa and the Indian sub-continent does not extend sufficiently to the north.
The LMA is based on a series of complex EOF (CEOF) computed on relatively small time sections (every 5 days on a 120-day time window) of the outgoing longwave radiation (OLR) time series. The first complex eigenvector best characterizes (in phase and amplitude) the intraseasonal fluctuation for the 120-day time section. The corresponding percentage of variance represents the degree of spatial organization of this event. The LMA retains only maxima in the time series of the percentage of variance. For JFM, the LMA extracts 41 events for 30 years of observations (NOAA OLR, Liebmann and Smith 1996), 52 events for 30 years of the IPSL-CM5A-LR run and 34 events for 25 years of the IPSL-CM5B-LR run. The average time-scale for these events is roughly 40 days for all three datasets. An average pattern is computed from the JFM events having a percentage of variance above the annual average.