## 6 Discussion
If just the error in band emissivity as input to the [PERSON] et al., 2001 or the Becker and Li 1990 would be comparable to the findings of this study, this lead to errors between 0.1 and 0.8 K. Relating this to the GCOS requirement of accuracy better than 1 K [23], it becomes clear that errors in surface emissivity may substantially lower the quality of a product. This study has shown that the different sensitivities amongst the split-window algorithms are more similar than the sensitivities of the different mono-window algorithms. In the presented case of Figure 15 lowest sensitivity is found for the [PERSON] and Li 1990 and the [PERSON] 1984 algorithms with a total mean sensitivity of 1.9 K for daytime and nighttime conditions. Highest total sensitivity is found for the [PERSON] and [PERSON] 2008 algorithm with 3.1 K for same conditions. Lowest sensitivity of the mono-window algorithms is found for the [PERSON] 1983 and the [PERSON] et al., 2001 algorithms with a total mean sensitivity of 1.3 K for daytime and nighttime conditions. Highest total sensitivity is found for the [PERSON] and Sobrino 2003 algorithm with a total sensitivity of 4.6 K for same conditions. Against the background of long time series and the situation of imperfect input data, the [PERSON] and Li 1990 and the [PERSON] 1984 split-window algorithms and the [PERSON] 1983 and the [PERSON] et al., 2001 algorithms are best suited for. The simulation of the impact of misclassification errors to estimated LST showed that mainly misclassification of bare rock and ground has a significant impact, as well as the class snow and ice in case of band 5. Any misclassification from one to another vegetation class resulted in emissivity errors of less than 0.01, which would result in LST errors of generally less than 0.3 K. To avoid large errors in LST due to misclassification it is suggested to perform an additional check for pixels, which are assigned to the classes bare rock/ground and snow based on classification maps or other procedures. Regarding the selection of suitable algorithms for time series processing, the synopsis of the performance and the sensitivity analysis following algorithm we suggest: For the mono-window algorithm the [PERSON] et al., 2001 is preferred due to its good performance and low sensitivity results. For the split-window algorithm the Becker and Li 1990 and the [PERSON] 1984 algorithm can be suggested showing also good performance and low sensitivity values. A first comparison with MODIS data using the Becker and Li 1990 approach combined with the Vegetation Cover Method (VCM) from [30] showed despite of some cloud contaminated pixels a good agreement with an overall MAD between AVHRR and MODIS of 1.8 K [K], and an overall standard deviation of 1.4 [K]. This showed a clear improvement over the original AVHRR LST approach assessed in [40], which had an overall MAD of 3.3 K.  
The processing has been performed by a preference of coefficients of case C (consideration of ranges for view angle, columnar water vapour and first guess LST). In case where no coefficients were available due to missing coefficients in the database, case B or even case A might have been used. This could happen in extreme situations (e.g., very cold and humid), where the database would not provide enough profiles to calculate coefficients. A selection of pixels, where case C is applied only, might improve further the comparison. Besides the selection of suitable algorithms, the quantitative results are relevant for the assessment of global warming. The IPCC report from 2014 [42] concludes an average global surface warming of 0.85 K over the period 1880 to 2012, while the period from 1983 to 2012 was likely to be the warmest period of the last 1400 years in the Northern Hemisphere. Warming per decade varied from almost 0 K to more than 0.2 K since 1951. The assessment of performance and sensitivity of AVHRR LST shows that LST derived from AVHRR is feasible under certain conditions only as input to global warming studies.