This paper presents a statistical analysis of cirrus clouds radiative effects from the analysis of MPLNET lidar measurements in Greenbelt, Maryland. The study is unique in the way that exploits a continuous database of 20 years of lidar measurements in this MPLNET station. MPLNET network is a very well stablished network for aerosol and clouds studies vertically resolved. The team has long experience in studying cirrus cloud radiative effects from MPLNET lidar measurements and here they present very well an extended study making use of their background. The paper is well-written, the methodology and results being well presented and discussed. Therefore, I believe that the paper deserves publication in ACP because of the unique findings in determining the role of cirrus clouds in radiative forcing. Particularly, I found very important the trends in radiative at the surface and top of the atmosphere, and the possible feedbacks mechanisms with the surface albedo. I agree that the findings presented in this work will serve to better understand climate projections. I only have some minor issues before the final publication of the manuscript

Lines 57-59: Please rephrase. It is not clear the direct link of cirrus clouds with numerical weather modeling on all scales and day-to-day simulations.

Lines 63-64: Please revise. It seems there are typos.

Lines 70-72: Please revise. IT is not clear the link of NASA MPLNET network with the Earth Observing System program

Line 85: Please replace ‘measurements’ by ‘lidar measurements’

Section 2.1: I miss a reference for the data. Are they publicly available?

Line 106: Can you specify what is the maximum signal attenuation?

Line 109: Can you specify what are reduce retrieval errors?

Lines 120 -122: I do not understand the use of CALIPSO data. Please clarify

Line 136: Please clarify what is and how do you use the uncertainties in the lidar signals.

Lines 140-142: Please, add references GCDM and COD detections. Also, please explain all acronyms.

Line 148: Dealing with multiple scattering is essential for cirrus characterization. I miss some explanation or appropriate references.

Line 150: Please, add reference for previous validation studies.

Figure 1: I do not understand well that data are average of at least 1000 cloud samples (85%)

Figure 3: What are the different colors of the histograms? Please improve Figure quality.

Section 2.4: To me this is part of the results section. I recommend thinking about moving this section to the results section.

Section 3 Long-Term Trend Analysis: To me this is part of the methodology. I would re-name this section as 2.5.

Line 253: Please give a reference for MTD.

Line 270. Can you give a brief explanation of how do you compute monthly-averaged daytime CRE observations?

Table 1: What are Z-value and S-value ?

Lines 335 – 385: I found many times ‘Albedo’ that should be ‘albedo’. Please, correct this typo.

Reviewer Comments – Manuscript Title: “Long-term trends in daytime cirrus cloud radiative effects”

General Assessment:

This manuscript presents a valuable long-term analysis of the radiative effects of daytime cirrus clouds over Greenbelt, Maryland, based on 20 years of data from the NASA MPLNET network. The topic is highly relevant for understanding cloud-radiation interactions and climate feedback mechanisms, and the dataset used is uniquely long and consistent. The study employs appropriate statistical methods and a well-established radiative transfer model (Fu-Liou-Gu), and the authors make a compelling case for the climatological significance of the observed trends.

However, the manuscript in its current form has several significant weaknesses that must be addressed before it can be considered for publication. These issues pertain to the clarity of the writing, the completeness and presentation of methods and results, and the interpretative depth of the discussion.

Major Comments

1. Writing and Clarity: The manuscript is written in overly complex and dense language, with long, multi-clause sentences and an excessive use of technical or florid expressions (e.g., “elucidates the long-term trends and intricate variability”). This detracts from the accessibility and readability of the paper, even for a specialized audience. Simplifying the prose and aiming for clarity over sophistication is strongly recommended.

There are several typographical issues (e.g., inconsistent spacing before parentheses, inconsistent unit formatting). Line 119 TOA cloud forcing? Or TOA CRE?

1. Missing or Ambiguous Information: Several terms and methods are introduced without sufficient explanation. For example, UCDM and GCDM are mentioned with minimal context; these should be described in full or properly referenced. Similarly, the phrase "modified seasonal MK test (?)" (line 229) contains a placeholder that needs correction.

Lines 120 -122: I do not understand the use of CALIPSO data, is it used to calculate DF to be included in the Equation (1), no spatial/temporal resolution or retrieval limitations are discussed. Please clarify…

Lines 130–140 Missing explanation of UCDM/GCDM      The UCDM and GCDM cloud detection methods are mentioned but not properly defined.           “…cloud layer height retrievals are carried out using the uncertainty-based cloud detection method (UCDM)…”

Figure captions should be more descriptive. Some do not adequately explain the content or significance of the figure. Figure 1: I do not understand… “data are average of at least 1000 cloud…” and Figure 3: What are the different colors of the histograms?

Data Processing Description: The criteria for filtering cirrus clouds—based on temperature thresholds and lidar ratio constraints—are mentioned but lack a clear operational explanation (e.g., what constitutes “bookend” ratios, how are other clouds discarded?).

1. Equation Formatting and Mathematical Notation: Equation (1) (Line 175) is not clearly presented, at least for me. The summation limits are ambiguous, and variable definitions such as COD_RFi, CRFi, and DF are not adequately explained. All equations should be clearly typeset and introduced with explicit definitions of all terms. Notation should be consistent throughout the manuscript. Also, there is no equation for the CRE calculations at Surface.

1. Uncertainty Quantification and Error Propagation: Although statistical significance (via p-values) is discussed throughout, the paper lacks an adequate treatment of uncertainty. There are no confidence intervals presented for trends, nor is there a discussion of error propagation in the derived quantities (e.g., CREs). The results would be strengthened substantially by a more rigorous uncertainty analysis.

E.g., Line ~275: “TOA30sr exhibits a Sen slope of -0.035 W m−2 yr−1”, no error information or discussion.

Line 285: “…in 2010, the instrument was upgraded to a newer version…” Instrument upgrade in 2010 is mentioned, but its effect on trends is not evaluated.  The potential for instrument bias (post-2010) is acknowledged but not addressed analytically. I suggested to Separate the analysis into pre-/post-2010 to ensure data consistency.

Section 4: Lack of confidence intervals, Trend analyses use p-values and Sen’s slope but do not show uncertainty visually.

The minimum detectable trend (MDT) approach is relevant and well justified, but not connected clearly to the actual trends reported in Section 4. I suggest to clarify how close observed trends are to the detection limit…

1. Surface Albedo and Feedback Mechanisms: The study attributes declining surface albedo to reduced snow/ice cover, which is plausible but remains speculative in the absence of supporting data. I suggest including direct observational evidence of snow/ice changes, or at least citing relevant supporting studies.

Line 322: “...suggests a decrease in snow cover, likely driven by regional warming...” Decrease in albedo is attributed to snow/ice loss without supporting data (e.g., MODIS or CERES).

While increased Solar Zenith Angle (SZA) trends are observed, the discussion does not attempt to quantify how this might skew radiative flux measurements. I sugest to conduct a sensitivity test to estimate the impact of SZA trends on radiative measurements.

• Comparative Context and Generalizability
  
  The study is based on a single site (Greenbelt, MD), yet broader implications for climate trends are suggested. It would greatly improve the manuscript to compare findings with relevant satellite observations, even if only qualitatively.

Recommendation: Major Revision

This manuscript offers valuable insights supported by a unique dataset. However, the issues raised above—especially regarding clarity, completeness of methods, and uncertainty treatment—require substantial revision. I encourage the authors to carefully address these comments and resubmit a revised version for further consideration.